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Repository: chanchanchan97/ICAFusion
Branch: main
Commit: 1eccff4edac6
Files: 134
Total size: 15.3 MB
Directory structure:
gitextract_37ncapyb/
├── .idea/
│ ├── .gitignore
│ ├── ICAFusion.iml
│ ├── deployment.xml
│ ├── inspectionProfiles/
│ │ ├── Project_Default.xml
│ │ └── profiles_settings.xml
│ ├── modules.xml
│ └── vcs.xml
├── LICENSE
├── README.md
├── confluence.py
├── data/
│ ├── GlobalWheat2020.yaml
│ ├── VisDrone.yaml
│ ├── argoverse_hd.yaml
│ ├── coco.yaml
│ ├── coco128.yaml
│ ├── hyp.finetune.yaml
│ ├── hyp.scratch.yaml
│ ├── hyp.scratch_VEDAI.yaml
│ ├── multispectral/
│ │ ├── CVC14.yaml
│ │ ├── FLIR-align-3class.yaml
│ │ ├── FLIR-align.yaml
│ │ ├── LLVIP.yaml
│ │ ├── VEDAI.yaml
│ │ └── kaist.yaml
│ ├── scripts/
│ │ ├── get_argoverse_hd.sh
│ │ ├── get_coco.sh
│ │ └── get_voc.sh
│ └── voc.yaml
├── descriptor/
│ ├── CFOG.py
│ ├── CFOG_matlab.p
│ ├── LSS.py
│ ├── denseLSS.m
│ └── mexCalcSsdescs1.mexw64
├── detect_twostream.py
├── evaluation_script/
│ ├── KAIST_annotation.json
│ ├── README.md
│ ├── __init__.py
│ ├── coco.py
│ ├── cocoeval.py
│ ├── evaluation_script.py
│ ├── null
│ └── state_of_arts/
│ ├── ARCNN_result.txt
│ ├── CIAN_result.txt
│ ├── MBNet_result.txt
│ ├── MLPD_result.json
│ ├── MLPD_result.txt
│ └── MSDS-RCNN_result.txt
├── global_var.py
├── gradcam_visual.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ ├── export.py
│ ├── gradcam.py
│ ├── hub/
│ │ ├── anchors.yaml
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov3-tiny.yaml
│ │ ├── yolov3.yaml
│ │ ├── yolov5-fpn.yaml
│ │ ├── yolov5-p2.yaml
│ │ ├── yolov5-p6.yaml
│ │ ├── yolov5-p7.yaml
│ │ ├── yolov5-panet.yaml
│ │ ├── yolov5l6.yaml
│ │ ├── yolov5m6.yaml
│ │ ├── yolov5s-transformer.yaml
│ │ ├── yolov5s6.yaml
│ │ └── yolov5x6.yaml
│ ├── transformer/
│ │ ├── yolov5_ResNet50_NiNfusion_FLIR.yaml
│ │ ├── yolov5_ResNet50_NiNfusion_kaist.yaml
│ │ ├── yolov5_ResNet50_Transfusion_FLIR.yaml
│ │ ├── yolov5_ResNet50_Transfusion_kaist.yaml
│ │ ├── yolov5_VGG16_NiNfusion_FLIR.yaml
│ │ ├── yolov5_VGG16_NiNfusion_kaist.yaml
│ │ ├── yolov5_VGG16_Transfusion_FLIR.yaml
│ │ ├── yolov5_VGG16_Transfusion_kaist.yaml
│ │ ├── yolov5l_Add_FLIR.yaml
│ │ ├── yolov5l_MobileViT_NiNfusion_FLIR.yaml
│ │ ├── yolov5l_NiNfusion_FLIR.yaml
│ │ ├── yolov5l_NiNfusion_LLVIP.yaml
│ │ ├── yolov5l_NiNfusion_VEDAI.yaml
│ │ ├── yolov5l_Transfusion_FLIR.yaml
│ │ ├── yolov5l_Transfusion_LLVIP.yaml
│ │ ├── yolov5l_Transfusion_VEDAI.yaml
│ │ ├── yolov5l_Transfusion_kaist.yaml
│ │ ├── yolov5m_Add_kaist.yaml
│ │ ├── yolov5m_NiNfusion_FLIR.yaml
│ │ ├── yolov5m_NiNfusion_kaist.yaml
│ │ ├── yolov5m_Transfusion_FLIR.yaml
│ │ ├── yolov5m_Transfusion_SeaDrone.yaml
│ │ ├── yolov5m_Transfusion_VEDAI.yaml
│ │ ├── yolov5m_Transfusion_kaist.yaml
│ │ ├── yolov5m_weightedAdd_kaist.yaml
│ │ ├── yolov5n_Add_kaist.yaml
│ │ ├── yolov5n_NiNfusion_FLIR.yaml
│ │ ├── yolov5n_Transfusion_FLIR.yaml
│ │ ├── yolov5n_Transfusion_kaist.yaml
│ │ ├── yolov5s_Add_kaist.yaml
│ │ ├── yolov5s_Transfusion_FLIR.yaml
│ │ └── yolov5s_Transfusion_kaist.yaml
│ ├── yolo.py
│ ├── yolo_test.py
│ ├── yolov5l.yaml
│ ├── yolov5m.yaml
│ ├── yolov5s.yaml
│ └── yolov5x.yaml
├── requirements.txt
├── test.py
├── train.py
└── utils/
├── __init__.py
├── activations.py
├── autoanchor.py
├── aws/
│ ├── __init__.py
│ ├── mime.sh
│ ├── resume.py
│ └── userdata.sh
├── confluence.py
├── datasets.py
├── flask_rest_api/
│ ├── example_request.py
│ └── restapi.py
├── general.py
├── google_app_engine/
│ ├── Dockerfile
│ ├── additional_requirements.txt
│ └── app.yaml
├── google_utils.py
├── gradcam.py
├── loss.py
├── metrics.py
├── plots.py
├── torch_utils.py
└── wandb_logging/
├── __init__.py
├── log_dataset.py
└── wandb_utils.py
================================================
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FILE: .idea/.gitignore
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# 基于编辑器的 HTTP 客户端请求
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<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
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<version value="1.0" />
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<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/ICAFusion.iml" filepath="$PROJECT_DIR$/.idea/ICAFusion.iml" />
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<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
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================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
## <div align="center">ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection</div>
### Introduction
In this paper, we propose a novel feature fusion framework of dual cross-attention transformers to model global feature interaction and capture complementary information across modalities simultaneously. In addition, we introdece an iterative interaction mechanism into dual cross-attention transformers, which shares parameters among block-wise multimodal transformers to reduce model complexity and computation cost. The proposed method is general and effective to be integrated into different detection frameworks and used with different backbones. Experimental results on KAIST, FLIR, and VEDAI datasets show that the proposed method achieves superior performance and faster inference, making it suitable for various practical scenarios.
Paper download in: https://arxiv.org/pdf/2308.07504.pdf
### Overview
<div align="center">
<img src="https://github.com/chanchanchan97/ICAFusion/assets/39607836/05a71809-0182-487d-9013-442497a996fd" width="600px">
<div style="color:orange; border-bottom: 10px solid #d9d9d9; display: inline-block; color: #999; padding: 10px;"> Fig 1. Overview of our multispectral object detection framework </div>
</div>
<div align="center">
<img src="https://github.com/chanchanchan97/ICAFusion/assets/39607836/b82ba614-22da-421c-89e9-53d6d535ee36" width="600px">
<div style="color:orange; border-bottom: 10px solid #d9d9d9; display: inline-block; color: #999; padding: 10px;"> Fig 2. Illustration of the proposed DMFF module </div>
</div>
### Installation
Clone repo and install requirements.txt in a Python>=3.8.0 conda environment, including PyTorch>=1.12.
```
git clone https://github.com/chanchanchan97/ICAFusion.git
cd ICAFusion
pip install -r requirements.txt
```
### Datasets
- **KAIST**
Link：https://pan.baidu.com/s/1UdwQJH-cHVL91pkMW-ij6g
Code：ig3y
- **FLIR-aligned**
Link：https://pan.baidu.com/s/1ljr8qJYdz-60Lj-iVEHBvg
Code：uqzs
- **VEDAI**
Link：https://pan.baidu.com/s/1UKSI0Go0Ddt62tXNIySz9w
Code：5ett
### Weights
- **KAIST**
Link：https://pan.baidu.com/s/18UXctOSgjp6EUcJXIGbWTQ
Code：9eku
- **FLIR-aligned**
We fixed some bugs and now update the results on the FLIR dataset with the model weights as follows:
~~Link：https://pan.baidu.com/s/1VZbsTE4o6bw2XBypPW3zoA
Code：xli9~~
Link: https://pan.baidu.com/s/1fEFOEyzVIxNCMjJjuujJUQ?pwd=a81f
code: a81f
### Results update
- **FLIR**
| Methods | person | car | bicycle | mAP@50 |
|----------------------|----------|----------|----------|----------|
| ~~yolov5_ICAFusion~~ | ~~81.6~~ | ~~89.0~~ | ~~66.9~~ | ~~79.2~~ |
| yolov5_ICAFusion | 84.9 | 89.8 | 73.8 | 82.8 |
- **M3FD**
| People | Car | Bus | Motorcycle | Lamp | Truck | mAP@50 |
|--------|------|------|------------|------|-------|--------|
| 82.3 | 93.4 | 93.9 | 89.6 | 82.2 | 87.8 | 88.2 |
- **DroneVehicle**
| Car | Truck | Bus | Van | freight_car | mAP@50 |
|------|-------|------|------|-------------|--------|
| 98.0 | 84.2 | 97.1 | 71.0 | 73.8 | 84.8 |
- **DVTOP**
| Car | bicycle | person | mAP@50 |
|------|------|-------------|--------|
| 92.6 |83.6|84.6 |86.9|
- **SeaDroneSee**
| Swimmer | Floater | Boat | mAP@50 |
|------|------|-------------|--------|
|14.7 |69.5|99.5 |61.2|
### Files
**Note**: This is the txt files for evaluation. We continuously optimize our codes, which results in the difference in detection performance. However, the codes of module for multimodal feature fusion still remain consistent with the methods proposed in this paper.
- **KAIST**
Link：https://pan.baidu.com/s/1N7SNEPXKX7KFaO2Th7vq2g
Code：zijw
### Our new works
- Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection **[[paper]](https://arxiv.org/abs/2507.14643)** **[[code]](https://github.com/61s61min/MS2Fusion)**
- IRDFusion: Iterative Relation-Map Difference guided Feature Fusion for Multispectral Object Detection **[[paper]](https://arxiv.org/html/2509.09085v1)** **[[code]](https://github.com/61s61min/IRDFusion.git)**
### Citation
If you find our work useful in your research, please consider citing:
```
@article{SHEN2023109913,
title={ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection},
author={Shen, Jifeng and Chen, Yifei and Liu, Yue and Zuo, Xin and Fan, Heng and Yang, Wankou},
journal={Pattern Recognition},
pages={109913},
year={2023},
issn={0031-3203},
doi={https://doi.org/10.1016/j.patcog.2023.109913},
author={Jifeng Shen and Yifei Chen and Yue Liu and Xin Zuo and Heng Fan and Wankou Yang},
}
```
================================================
FILE: confluence.py
================================================
"""
Author: Andrew Shepley
Contact: asheple2@une.edu.au
Source: Confluence
Methods
a) assign_boxes_to_classes
b) normalise_coordinates
c) confluence_nms - returns maxima scoring box, removes false positives using confluence - efficient
d) confluence - returns most confluent box, removes false positives using confluence - less efficient but better box
"""
from collections import defaultdict
import numpy as np
def assign_boxes_to_classes(bounding_boxes, classes, scores):
"""
Parameters:
bounding_boxes: list of bounding boxes (x1,y1,x2,y2)
classes: list of class identifiers (int value, e.g. 1 = person)
scores: list of class confidence scores (0.0-1.0)
Returns:
boxes_to_classes: defaultdict(list) containing mapping to bounding boxes and confidence scores to class
"""
boxes_to_classes = defaultdict(list)
for each_box, each_class, each_score in zip(bounding_boxes, classes, scores):
if each_score >= 0.05:
boxes_to_classes[each_class].append(np.array([each_box[0],each_box[1],each_box[2],each_box[3], each_score]))
return boxes_to_classes
def normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y):
"""
Parameters:
x1, y1, x2, y2: bounding box coordinates to normalise
min_x,max_x,min_y,max_y: minimum and maximum bounding box values (min = 0, max = 1)
Returns:
Normalised bounding box coordinates (scaled between 0 and 1)
"""
x1, y1, x2, y2 = (x1-min_x)/(max_x-min_x), (y1-min_y)/(max_y-min_y), (x2-min_x)/(max_x-min_x), (y2-min_y)/(max_y-min_y)
return x1, y1, x2, y2
def confluence_nms(bounding_boxes,scores,classes,confluence_thr,gaussian,score_thr=0.05,sigma=0.5):
"""
Parameters:
bounding_boxes: list of bounding boxes (x1,y1,x2,y2)
classes: list of class identifiers (int value, e.g. 1 = person)
scores: list of class confidence scores (0.0-1.0)
confluence_thr: value between 0 and 2, with optimum from 0.5-0.8
gaussian: boolean switch to turn gaussian decaying of suboptimal bounding box confidence scores (setting to False results in suppression of suboptimal boxes)
score_thr: class confidence score
sigma: used in gaussian decaying. A smaller value causes harsher decaying.
Returns:
output: A dictionary mapping class identity to final retained boxes (and corresponding confidence scores)
"""
class_mapping = assign_boxes_to_classes(bounding_boxes, classes, scores)
output = {}
for each_class in class_mapping:
dets = np.array(class_mapping[each_class])
retain = []
while dets.size > 0:
max_idx = np.argmax(dets[:, 4], axis=0)
dets[[0, max_idx], :] = dets[[max_idx, 0], :]
retain.append(dets[0, :])
x1, y1, x2, y2 = dets[0, 0], dets[0, 1], dets[0, 2], dets[0, 3]
min_x = np.minimum(x1, dets[1:, 0])
min_y = np.minimum(y1, dets[1:, 1])
max_x = np.maximum(x2, dets[1:, 2])
max_y = np.maximum(y2, dets[1:, 3])
x1, y1, x2, y2 = normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y)
xx1, yy1, xx2, yy2 = normalise_coordinates(dets[1:, 0], dets[1:, 1], dets[1:, 2], dets[1:, 3],min_x,max_x,min_y,max_y)
md_x1,md_x2,md_y1,md_y2 = abs(x1-xx1),abs(x2-xx2),abs(y1-yy1),abs(y2-yy2)
manhattan_distance = (md_x1+md_x2+md_y1+md_y2)
weights = np.ones_like(manhattan_distance)
if (gaussian == True):
gaussian_weights = np.exp(-((1-manhattan_distance) * (1-manhattan_distance)) / sigma)
weights[manhattan_distance<=confluence_thr]=gaussian_weights[manhattan_distance<=confluence_thr]
else:
weights[manhattan_distance<=confluence_thr]=manhattan_distance[manhattan_distance<=confluence_thr]
dets[1:, 4] *= weights
to_reprocess = np.where(dets[1:, 4] >= score_thr)[0]
dets = dets[to_reprocess + 1, :]
output[each_class]=retain
return output
def confluence(bounding_boxes,scores,classes,confluence_thr,gaussian,score_thr=0.05,sigma=0.5):
"""
Parameters:
bounding_boxes: list of bounding boxes (x1,y1,x2,y2)
classes: list of class identifiers (int value, e.g. 1 = person)
scores: list of class confidence scores (0.0-1.0)
confluence_thr: value between 0 and 2, with optimum from 0.5-0.8
gaussian: boolean switch to turn gaussian decaying of suboptimal bounding box confidence scores (setting to False results in suppression of suboptimal boxes)
score_thr: class confidence score
sigma: used in gaussian decaying. A smaller value causes harsher decaying.
Returns:
output: A dictionary mapping class identity to final retained boxes (and corresponding confidence scores)
"""
class_mapping = assign_boxes_to_classes(bounding_boxes, classes, scores)
output = {}
for each_class in class_mapping:
dets = np.array(class_mapping[each_class])
retain = []
while dets.size > 0:
confluence_scores,proximities = [],[]
while len(confluence_scores)<np.size(dets,0):
current_box = len(confluence_scores)
x1, y1, x2, y2 = dets[current_box, 0], dets[current_box, 1], dets[current_box, 2], dets[current_box, 3]
confidence_score = dets[current_box, 4]
xx1,yy1,xx2,yy2,cconf = dets[np.arange(len(dets))!=current_box, 0],dets[np.arange(len(dets))!=current_box, 1],dets[np.arange(len(dets))!=current_box, 2],dets[np.arange(len(dets))!=current_box, 3],dets[np.arange(len(dets))!=current_box, 4]
min_x,min_y,max_x,max_y = np.minimum(x1, xx1),np.minimum(y1, yy1),np.maximum(x2, xx2),np.maximum(y2, yy2)
x1, y1, x2, y2 = normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y)
xx1, yy1, xx2, yy2 = normalise_coordinates(xx1, yy1, xx2, yy2,min_x,max_x,min_y,max_y)
hd_x1,hd_x2,vd_y1,vd_y2 = abs(x1-xx1),abs(x2-xx2),abs(y1-yy1),abs(y2-yy2)
proximity = (hd_x1+hd_x2+vd_y1+vd_y2)
all_proximities = np.ones_like(proximity)
cconf_scores = np.zeros_like(cconf)
all_proximities[proximity <= confluence_thr] = proximity[proximity <= confluence_thr]
cconf_scores[proximity <= confluence_thr]=cconf[proximity <= confluence_thr]
if(cconf_scores.size>0):
confluence_score = np.amax(cconf_scores)
else:
confluence_score = confidence_score
if(all_proximities.size>0):
proximity = (sum(all_proximities)/all_proximities.size)*(1-confidence_score)
else:
proximity = sum(all_proximities)*(1-confidence_score)
confluence_scores.append(confluence_score)
proximities.append(proximity)
conf = np.array(confluence_scores)
prox = np.array(proximities)
dets_temp = np.concatenate((dets, prox[:, None]), axis=1)
dets_temp = np.concatenate((dets_temp, conf[:, None]), axis=1)
min_idx = np.argmin(dets_temp[:, 5], axis=0)
dets[[0, min_idx], :] = dets[[min_idx, 0], :]
dets_temp[[0, min_idx], :] = dets_temp[[min_idx, 0], :]
dets[0,4]=dets_temp[0,6]
retain.append(dets[0, :])
x1, y1, x2, y2 = dets[0, 0], dets[0, 1], dets[0, 2], dets[0, 3]
min_x = np.minimum(x1, dets[1:, 0])
min_y = np.minimum(y1, dets[1:, 1])
max_x = np.maximum(x2, dets[1:, 2])
max_y = np.maximum(y2, dets[1:, 3])
x1, y1, x2, y2 = normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y)
xx1, yy1, xx2, yy2 = normalise_coordinates(dets[1:, 0], dets[1:, 1], dets[1:, 2], dets[1:, 3],min_x,max_x,min_y,max_y)
md_x1,md_x2,md_y1,md_y2 = abs(x1-xx1),abs(x2-xx2),abs(y1-yy1),abs(y2-yy2)
manhattan_distance = (md_x1+md_x2+md_y1+md_y2)
weights = np.ones_like(manhattan_distance)
if (gaussian == True):
gaussian_weights = np.exp(-((1-manhattan_distance) * (1-manhattan_distance)) / sigma)
weights[manhattan_distance<=confluence_thr]=gaussian_weights[manhattan_distance<=confluence_thr]
else:
weights[manhattan_distance<=confluence_thr]=manhattan_distance[manhattan_distance<=confluence_thr]
dets[1:, 4] *= weights
to_reprocess = np.where(dets[1:, 4] >= score_thr)[0]
dets = dets[to_reprocess + 1, :]
output[each_class]=retain
return output
================================================
FILE: data/GlobalWheat2020.yaml
================================================
# Global Wheat 2020 dataset http://www.global-wheat.com/
# Train command: python train.py --data GlobalWheat2020.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /datasets/GlobalWheat2020
# /yolov5
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: # 3422 images
- ../datasets/GlobalWheat2020/images/arvalis_1
- ../datasets/GlobalWheat2020/images/arvalis_2
- ../datasets/GlobalWheat2020/images/arvalis_3
- ../datasets/GlobalWheat2020/images/ethz_1
- ../datasets/GlobalWheat2020/images/rres_1
- ../datasets/GlobalWheat2020/images/inrae_1
- ../datasets/GlobalWheat2020/images/usask_1
val: # 748 images (WARNING: train set contains ethz_1)
- ../datasets/GlobalWheat2020/images/ethz_1
test: # 1276
- ../datasets/GlobalWheat2020/images/utokyo_1
- ../datasets/GlobalWheat2020/images/utokyo_2
- ../datasets/GlobalWheat2020/images/nau_1
- ../datasets/GlobalWheat2020/images/uq_1
# number of classes
nc: 1
# class names
names: [ 'wheat_head' ]
# download command/URL (optional) --------------------------------------------------------------------------------------
download: |
from utils.general import download, Path
# Download
dir = Path('../datasets/GlobalWheat2020') # dataset directory
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
download(urls, dir=dir)
# Make Directories
for p in 'annotations', 'images', 'labels':
(dir / p).mkdir(parents=True, exist_ok=True)
# Move
for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
(dir / p).rename(dir / 'images' / p) # move to /images
f = (dir / p).with_suffix('.json') # json file
if f.exists():
f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
================================================
FILE: data/VisDrone.yaml
================================================
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
# Train command: python train.py --data VisDrone.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /VisDrone
# /yolov5
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images
val: ../VisDrone/VisDrone2019-DET-val/images # 548 images
test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images
# number of classes
nc: 10
# class names
names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
# download command/URL (optional) --------------------------------------------------------------------------------------
download: |
from utils.general import download, os, Path
def visdrone2yolo(dir):
from PIL import Image
from tqdm import tqdm
def convert_box(size, box):
# Convert VisDrone box to YOLO xywh box
dw = 1. / size[0]
dh = 1. / size[1]
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
for f in pbar:
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
lines = []
with open(f, 'r') as file: # read annotation.txt
for row in [x.split(',') for x in file.read().strip().splitlines()]:
if row[4] == '0': # VisDrone 'ignored regions' class 0
continue
cls = int(row[5]) - 1
box = convert_box(img_size, tuple(map(int, row[:4])))
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
fl.writelines(lines) # write label.txt
# Download
dir = Path('../VisDrone') # dataset directory
urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
download(urls, dir=dir)
# Convert
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
================================================
FILE: data/argoverse_hd.yaml
================================================
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
# Train command: python train.py --data argoverse_hd.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /argoverse
# /yolov5
# download command/URL (optional)
download: bash data/scripts/get_argoverse_hd.sh
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images
val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges
test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview
# number of classes
nc: 8
# class names
names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ]
================================================
FILE: data/coco.yaml
================================================
# COCO 2017 dataset http://cocodataset.org
# Train command: python train.py --data coco.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco
# /yolov5
# download command/URL (optional)
download: bash data/scripts/get_coco.sh
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../coco/train2017.txt # 118287 images
val: ../coco/val2017.txt # 5000 images
test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# number of classes
nc: 80
# class names
names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush' ]
# Print classes
# with open('data/coco.yaml') as f:
# d = yaml.safe_load(f) # dict
# for i, x in enumerate(d['names']):
# print(i, x)
================================================
FILE: data/coco128.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../coco128/images/train2017/ # 128 images
val: ../coco128/images/train2017/ # 128 images
# number of classes
nc: 80
# class names
names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush' ]
================================================
FILE: data/hyp.finetune.yaml
================================================
# Hyperparameters for VOC finetuning
# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
# Hyperparameter Evolution Results
# Generations: 306
# P R mAP.5 mAP.5:.95 box obj cls
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
lr0: 0.02 #(0.0032)
lrf: 0.12
momentum: 0.843
weight_decay: 0.00036
warmup_epochs: 2.0
warmup_momentum: 0.5
warmup_bias_lr: 0.05
box: 0.0296
cls: 0.243
cls_pw: 0.631
obj: 0.301
obj_pw: 0.911
iou_t: 0.2
anchor_t: 2.91
# anchors: 3.63
fl_gamma: 0.0
hsv_h: 0.0138
hsv_s: 0.664
hsv_v: 0.464
degrees: 0.373
translate: 0.245
scale: 0.898
shear: 0.602
perspective: 0.0
flipud: 0.00856
fliplr: 0.5
mosaic: 1.0
mixup: 0.243
================================================
FILE: data/hyp.scratch.yaml
================================================
# Hyperparameters for COCO training from scratch
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate ( original SGD=1E-2, Adam=1E-3)
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
================================================
FILE: data/hyp.scratch_VEDAI.yaml
================================================
# Hyperparameters for COCO training from scratch
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate ( original SGD=1E-2, Adam=1E-3)
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
================================================
FILE: data/multispectral/CVC14.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/CVC14
train_rgb: /home/shen/Chenyf/CVC14/visible/train # 128 images
val_rgb: /home/shen/Chenyf/CVC14/visible/test # 128 images
train_ir: /home/shen/Chenyf/CVC14/infrared/train # 128 images
val_ir: /home/shen/Chenyf/CVC14/infrared/test # 128 images
# number of classes
nc: 1
# class names
names: ['person']
================================================
FILE: data/multispectral/FLIR-align-3class.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/FLIR-align-3class
train_rgb: /home/shen/Chenyf/FLIR-align-3class/visible/train # 128 images
val_rgb: /home/shen/Chenyf/FLIR-align-3class/visible/test # 128 images
train_ir: /home/shen/Chenyf/FLIR-align-3class/infrared/train # 128 images
val_ir: /home/shen/Chenyf/FLIR-align-3class/infrared/test # 128 images
# number of classes
nc: 3
# class names
names: ['person', 'car', 'bicycle']
================================================
FILE: data/multispectral/FLIR-align.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/FLIR-align
train_rgb: /home/shen/Chenyf/FLIR-align/visible/train # 128 images
val_rgb: /home/shen/Chenyf/FLIR-align/visible/test # 128 images
train_ir: /home/shen/Chenyf/FLIR-align/infrared/train # 128 images
val_ir: /home/shen/Chenyf/FLIR-align/infrared/test # 128 images
# number of classes
nc: 1
# class names
names: ['person']
================================================
FILE: data/multispectral/LLVIP.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/LLVIP
train_rgb: /home/shen/Chenyf/LLVIP/visible/train/ # 128 images
val_rgb: /home/shen/Chenyf/LLVIP/visible/test/ # 128 images
train_ir: /home/shen/Chenyf/LLVIP/infrared/train/ # 128 images
val_ir: /home/shen/Chenyf/LLVIP/infrared/test/ # 128 images
# number of classes
nc: 1
# class names
names: ['person']
================================================
FILE: data/multispectral/VEDAI.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/VEDAI/fold01
train_rgb: /home/shen/Chenyf/VEDAI/fold01/visible/train # 128 images
val_rgb: /home/shen/Chenyf/VEDAI/fold01/visible/test # 128 images
train_ir: /home/shen/Chenyf/VEDAI/fold01/infrared/train # 128 images
val_ir: /home/shen/Chenyf/VEDAI/fold01/infrared/test # 128 images
# number of classes
nc: 9
# class names
names: ['car', 'truck', 'pickup', 'tractor', 'camper', 'ship', 'van', 'plane', 'other']
================================================
FILE: data/multispectral/kaist.yaml
================================================
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: /home/shen/Chenyf/kaist
train_rgb: /home/shen/Chenyf/kaist/visible/train/ # 128 images
val_rgb: /home/shen/Chenyf/kaist/visible/test/ # 128 images
train_ir: /home/shen/Chenyf/kaist/infrared/train/ # 128 images
val_ir: /home/shen/Chenyf/kaist/infrared/test/ # 128 images
# number of classes
nc: 1
# class names
names: ['person']
================================================
FILE: data/scripts/get_argoverse_hd.sh
================================================
#!/bin/bash
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
# Download command: bash data/scripts/get_argoverse_hd.sh
# Train command: python train.py --data argoverse_hd.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /argoverse
# /yolov5
# Download/unzip images
d='../argoverse/' # unzip directory
mkdir $d
url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
f=Argoverse-HD-Full.zip
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
wait # finish background tasks
cd ../argoverse/Argoverse-1.1/
ln -s tracking images
cd ../Argoverse-HD/annotations/
python3 - "$@" <<END
import json
from pathlib import Path
annotation_files = ["train.json", "val.json"]
print("Converting annotations to YOLOv5 format...")
for val in annotation_files:
a = json.load(open(val, "rb"))
label_dict = {}
for annot in a['annotations']:
img_id = annot['image_id']
img_name = a['images'][img_id]['name']
img_label_name = img_name[:-3] + "txt"
obj_class = annot['category_id']
x_center, y_center, width, height = annot['bbox']
x_center = (x_center + width / 2) / 1920. # offset and scale
y_center = (y_center + height / 2) / 1200. # offset and scale
width /= 1920. # scale
height /= 1200. # scale
img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
Path(img_dir).mkdir(parents=True, exist_ok=True)
if img_dir + "/" + img_label_name not in label_dict:
label_dict[img_dir + "/" + img_label_name] = []
label_dict[img_dir + "/" + img_label_name].append(f"{obj_class} {x_center} {y_center} {width} {height}\n")
for filename in label_dict:
with open(filename, "w") as file:
for string in label_dict[filename]:
file.write(string)
END
mv ./labels ../../Argoverse-1.1/
================================================
FILE: data/scripts/get_coco.sh
================================================
#!/bin/bash
# COCO 2017 dataset http://cocodataset.org
# Download command: bash data/scripts/get_coco.sh
# Train command: python train.py --data coco.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco
# /yolov5
# Download/unzip labels
d='../' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
# Download/unzip images
d='../coco/images' # unzip directory
url=http://images.cocodataset.org/zips/
f1='train2017.zip' # 19G, 118k images
f2='val2017.zip' # 1G, 5k images
f3='test2017.zip' # 7G, 41k images (optional)
for f in $f1 $f2; do
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
done
wait # finish background tasks
================================================
FILE: data/scripts/get_voc.sh
================================================
#!/bin/bash
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Download command: bash data/scripts/get_voc.sh
# Train command: python train.py --data voc.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /VOC
# /yolov5
start=$(date +%s)
mkdir -p ../tmp
cd ../tmp/
# Download/unzip images and labels
d='.' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
for f in $f3 $f2 $f1; do
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
done
wait # finish background tasks
end=$(date +%s)
runtime=$((end - start))
echo "Completed in" $runtime "seconds"
echo "Splitting dataset..."
python3 - "$@" <<END
import os
import xml.etree.ElementTree as ET
from os import getcwd
sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
def convert_box(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
return x * dw, y * dh, w * dw, h * dh
def convert_annotation(year, image_id):
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
bb = convert_box((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
cwd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOCdevkit/VOC%s/labels/' % year):
os.makedirs('VOCdevkit/VOC%s/labels/' % year)
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()
list_file = open('%s_%s.txt' % (year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (cwd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
END
cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
python3 - "$@" <<END
import os
print(os.path.exists('../tmp/train.txt'))
with open('../tmp/train.txt', 'r') as f:
for line in f.readlines():
line = "/".join(line.split('/')[-5:]).strip()
if os.path.exists("../" + line):
os.system("cp ../" + line + " ../VOC/images/train")
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
if os.path.exists("../" + line):
os.system("cp ../" + line + " ../VOC/labels/train")
print(os.path.exists('../tmp/2007_test.txt'))
with open('../tmp/2007_test.txt', 'r') as f:
for line in f.readlines():
line = "/".join(line.split('/')[-5:]).strip()
if os.path.exists("../" + line):
os.system("cp ../" + line + " ../VOC/images/val")
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
if os.path.exists("../" + line):
os.system("cp ../" + line + " ../VOC/labels/val")
END
rm -rf ../tmp # remove temporary directory
echo "VOC download done."
================================================
FILE: data/voc.yaml
================================================
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Train command: python train.py --data voc.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /VOC
# /yolov5
# download command/URL (optional)
download: bash data/scripts/get_voc.sh
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../VOC/images/train/ # 16551 images
val: ../VOC/images/val/ # 4952 images
# number of classes
nc: 20
# class names
names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]
================================================
FILE: descriptor/CFOG.py
================================================
import torch
import numpy as np
#import matlab.engine
import torch.nn.functional as F
from PIL import Image
import scipy.io as sio
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def denseCFOG(image):
s_im = image.shape
if len(s_im) == 2:
des_tensor = denseCFOG2D(image)
elif len(s_im) == 3:
assert s_im[1] == 1
image = image.squeeze(0)
des_tensor = denseCFOG2D(image)
elif len(s_im) == 4:
batchSize = s_im[0]
assert s_im[1] == 1
des_tensor = torch.zeros(batchSize, 9, s_im[2], s_im[3]).to(device)
for b in range(batchSize):
des_tensor[b] = denseCFOG2D(image[b].squeeze(0))
else:
des_tensor = 0
return des_tensor
def denseCFOG2D(image):
eng = matlab.engine.start_matlab()
eng.cd('./descriptor', nargout=0)
im_np = np.array(image.detach().cpu())
im_np = (im_np * 255).astype(np.uint8)
im_matlab = matlab.uint8(im_np.tolist())
des_matlab = eng.CFOG_matlab(im_matlab)
des_np = np.array(des_matlab)
des_tensor = torch.tensor(des_np, dtype=torch.float32).to(device).permute(2, 0, 1)
eng.exit()
return des_tensor
================================================
FILE: descriptor/LSS.py
================================================
import torch
import numpy as np
#import matlab.engine
#eng = matlab.engine.start_matlab()
#eng.cd('./descriptor',nargout=0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def denseLSS(image):
s_im = image.shape
if len(s_im) == 2:
des_tensor = denseLSS2D(image)
elif len(s_im) == 3:
assert s_im[1] == 1
image = image.squeeze(0)
des_tensor = denseLSS2D(image)
elif len(s_im) == 4:
batchSize = s_im[0]
assert s_im[1] == 1
des_tensor = torch.zeros(batchSize, 18, s_im[2], s_im[3]).to(device)
for b in range(batchSize):
des_tensor[b] = denseLSS2D(image[b].squeeze(0))
else:
des_tensor = 0
return des_tensor
def denseLSS2D(image):
im_np = np.array(image.detach().cpu())
im_np = (im_np * 255).astype(np.uint8)
im_matlab = matlab.uint8(im_np.tolist())
des_matlab = eng.denseLSS(im_matlab, 3.0, 2.0, 9.0)
des_np = np.array(des_matlab)
des_tensor = torch.tensor(des_np, dtype=torch.float32).to(device)
return des_tensor
def denseLSS_matlab(image):
s_im = image.shape
if len(s_im) == 2:
des_matlab = denseLSS2D_matlab(image)
elif len(s_im) == 3:
assert s_im[1] == 1
image = image.squeeze(0)
des_matlab = denseLSS2D_matlab(image)
elif len(s_im) == 4:
batchSize = s_im[0]
assert s_im[1] == 1
des_matlab = eng.zeros(batchSize, 18, s_im[2], s_im[3])
for b in range(batchSize):
des_matlab[b] = denseLSS2D_matlab(image[b].squeeze(0))
else:
des_matlab = 0
return des_matlab
def denseLSS2D_matlab(image):
im_np = np.array(image.detach().cpu())
im_np = (im_np * 255).astype(np.uint8)
im_matlab = matlab.uint8(im_np.tolist())
des_matlab = eng.denseLSS(im_matlab, 3.0, 2.0, 9.0)
return des_matlab
================================================
FILE: descriptor/denseLSS.m
================================================
function des = denseLSS(img,desc_rad,nrad,nang);
parms.patch_size=3;
parms.desc_rad=desc_rad;
parms.nrad=nrad;
parms.nang=nang;
parms.var_noise=3000;
parms.saliency_thresh = 1;
%parms.saliency_thresh = 0.7;
parms.homogeneity_thresh=1;
%parms.homogeneity_thresh=0.7;
parms.snn_thresh=1; % I usually disable saliency checking
%parms.snn_thresh=0.85;
%parms.nChannels=size(i,3);
des_num = parms.nrad*parms.nang;
%des_num = (2*desc_rad+1)*(2*desc_rad+1);
margin = parms.desc_rad + (parms.patch_size-1)/2;
img = padarray(img,[margin,margin],'symmetric');
img = double(img(:,:,1));
[h,w] = size(img);
des_width = w-2*margin; % the width of descriptor
des_height = h-2*margin;% the height of descriptor
destmp = zeros(des_height,des_width,des_num);
des = zeros(h,w,des_num);
%[resp, draw_coords, salient_coords, homogeneous_coords, snn_coords] = mexCalcSsdescs(img, parms);
[resp, draw_coords, salient_coords, homogeneous_coords, snn_coords] = mexCalcSsdescs1(img, parms);
%[resp, draw_coords, salient_coords, homogeneous_coords, snn_coords] = mexCalcSsdescs_mean(img, parms);
%[resp, draw_coords, salient_coords, homogeneous_coords, snn_coords] = mexCalcSSD(img, parms);
%[resp, draw_coords, salient_coords, homogeneous_coords, snn_coords] = mexCalcSSDslow(img, parms);
resp = resp';
temp = reshape(resp,[des_width,des_height,des_num]);
temp1 = permute(temp,[2 1 3]);
destmp = temp1;
%des(margin:h-margin-1,margin:w-margin-1,:) = destmp;
%des(margin+1:h-margin,margin+1:w-margin,:) = destmp;
%des = single(des);
des =single(destmp);
des = permute(des, [3 1 2]);
================================================
FILE: detect_twostream.py
================================================
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box, xywh2xyxy
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(opt):
source1, source2, weights, view_img, save_txt, imgsz = opt.source1, opt.source2, opt.weights, opt.view_img, opt.save_txt, opt.img_size
save_img = not opt.nosave and not source1.endswith('.txt') # save inference images
webcam = source1.isnumeric() or source1.endswith('.txt') or source1.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# Directories
save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
# imgsz = check_img_size(imgsz, s=stride) # check img_size
names = model.module.names if hasattr(model, 'module') else model.names # get class names
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source1, img_size=imgsz, stride=stride)
else:
dataset = LoadImages(source1, img_size=imgsz, stride=stride)
dataset2 = LoadImages(source2, img_size=imgsz, stride=stride)
# # Run inference
# if device.type != 'cpu':
# model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
img_num = 0
fps_sum = 0
for (path, img, im0s, vid_cap), (path_, img2, im0s_, vid_cap_) in zip(dataset, dataset2):
# img0 = img[:, :, ::-1].transpose(1, 2, 0)
# img0_ = img2[:, :, ::-1].transpose(1, 2, 0)
img = torch.from_numpy(img).to(device)
img2 = torch.from_numpy(img2).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
img2 = img2.half() if half else img2.float() # uint8 to fp16/32
img2 /= 255.0 # 0 - 255 to 0.0 - 1.0
if img2.ndimension() == 3:
img2 = img2.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, img2, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
p, s, im0_, frame = path, '', im0s_.copy(), getattr(dataset2, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
# print(gn)
mem = '%.4gM' % (torch.cuda.memory_reserved() / 1E6 if torch.cuda.is_available() else 0)
print('GPU Memory:', mem)
# # ----------------------------------------------------------------------------
# # 画GT，替换det
# #
# # ---------------------------------------------------------------------------
# annoPath = "/home/fqy/proj/paper/test_result/gt/"
# annoName = (path_.split("/")[-1]).split(".")[0] + ".txt"
# annoPath += annoName
# # print(annoPath)
# gt = np.loadtxt(annoPath)
# gt = gt.reshape((-1, 5))
# ones = np.ones((gt.shape[0], 1))
# gt = np.hstack((gt, ones))
# gt[:, [0,1,2,3,4,5]] = gt[:, [1,2,3,4,5,0]]
# gt = torch.from_numpy(gt).to(device)
# # print(gt[:, :4])
# gt[:, :4] = xywh2xyxy(gt[:, :4]) * 640
#
# det = gt
# print(det)
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or opt.save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
plot_one_box(xyxy, im0_, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
if opt.save_crop:
save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.6f}s, {1/(t2 - t1):.6f}Hz)')
# add all the fps
img_num += 1
fps_sum += 1/(t2 - t1)
# Stream results
if view_img:
cv2.imshow(str(p), img0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
save_path_rgb = save_path.split('.')[0] + '_rgb.' + save_path.split('.')[1]
save_path_ir = save_path.split('.')[0] + '_ir.' + save_path.split('.')[1]
print(save_path_rgb)
cv2.imwrite(save_path_rgb, im0)
cv2.imwrite(save_path_ir, im0_)
else: # 'video' or 'stream'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
print(f'Done. ({time.time() - t0:.3f}s)')
print(f'Average Speed: {fps_sum/img_num:.6f}Hz')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='/home/ubuntu/Chenyf/multispectral-object-detection/runs/train/exp6/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--source1', type=str, default='/home/shen/Chenyf/FLIR-align-3class/visible/test/', help='source') # file/folder, 0 for webcam
parser.add_argument('--source2', type=str, default='/home/shen/Chenyf/FLIR-align-3class/infrared/test/', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.1, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', default=False, action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--line-thickness', default=2, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=True, action='store_true', help='hide confidences')
opt = parser.parse_args()
print(opt)
check_requirements(exclude=('pycocotools', 'thop'))
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect(opt=opt)
strip_optimizer(opt.weights)
else:
print("helloxxxxxxxxxxxxxxxxxxxx")
detect(opt=opt)
================================================
FILE: evaluation_script/KAIST_annotation.json
================================================
{
"info": {
"dataset": "KAIST Multispectral Pedestrian Benchmark",
"url": "https://soonminhwang.github.io/rgbt-ped-detection/",
"related_project_url": "http://multispectral.kaist.ac.kr",
"publish": "CVPR 2015"
},
"info_improved": {
"sanitized_annotation": {
"publish": "BMVC 2018",
"url": "https://li-chengyang.github.io/home/MSDS-RCNN/",
"target": "files in train-all-02.txt (set00-set05)"
},
"improved_annotation": {
"url": "https://github.com/denny1108/multispectral-pedestrian-py-faster-rcnn",
"publish": "BMVC 2016",
"target": "files in test-all-20.txt (set06-set11)"
}
},
"images": [
{
"id": 0,
"im_name": "set06/V000/I00019",
"height": 512,
"width": 640
},
{
"id": 1,
"im_name": "set06/V000/I00039",
"height": 512,
"width": 640
},
{
"id": 2,
"im_name": "set06/V000/I00059",
"height": 512,
"width": 640
},
{
"id": 3,
"im_name": "set06/V000/I00079",
"height": 512,
"width": 640
},
{
"id": 4,
"im_name": "set06/V000/I00099",
"height": 512,
"width": 640
},
{
"id": 5,
"im_name": "set06/V000/I00119",
"height": 512,
"width": 640
},
{
"id": 6,
"im_name": "set06/V000/I00139",
"height": 512,
"width": 640
},
{
"id": 7,
"im_name": "set06/V000/I00159",
"height": 512,
"width": 640
},
{
"id": 8,
"im_name": "set06/V000/I00179",
"height": 512,
"width": 640
},
{
"id": 9,
"im_name": "set06/V000/I00199",
"height": 512,
"width": 640
},
{
"id": 10,
"im_name": "set06/V000/I00219",
"height": 512,
"width": 640
},
{
"id": 11,
"im_name": "set06/V000/I00239",
"height": 512,
"width": 640
},
{
"id": 12,
"im_name": "set06/V000/I00259",
"height": 512,
"width": 640
},
{
"id": 13,
"im_name": "set06/V000/I00279",
"height": 512,
"width": 640
},
{
"id": 14,
"im_name": "set06/V000/I00299",
"height": 512,
"width": 640
},
{
"id": 15,
"im_name": "set06/V000/I00319",
"height": 512,
"width": 640
},
{
"id": 16,
"im_name": "set06/V000/I00339",
"height": 512,
"width": 640
},
{
"id": 17,
"im_name": "set06/V000/I00359",
"height": 512,
"width": 640
},
{
"id": 18,
"im_name": "set06/V000/I00379",
"height": 512,
"width": 640
},
{
"id": 19,
"im_name": "set06/V000/I00399",
"height": 512,
"width": 640
},
{
"id": 20,
"im_name": "set06/V000/I00419",
"height": 512,
"width": 640
},
{
"id": 21,
"im_name": "set06/V000/I00439",
"height": 512,
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gitextract_37ncapyb/
├── .idea/
│ ├── .gitignore
│ ├── ICAFusion.iml
│ ├── deployment.xml
│ ├── inspectionProfiles/
│ │ ├── Project_Default.xml
│ │ └── profiles_settings.xml
│ ├── modules.xml
│ └── vcs.xml
├── LICENSE
├── README.md
├── confluence.py
├── data/
│ ├── GlobalWheat2020.yaml
│ ├── VisDrone.yaml
│ ├── argoverse_hd.yaml
│ ├── coco.yaml
│ ├── coco128.yaml
│ ├── hyp.finetune.yaml
│ ├── hyp.scratch.yaml
│ ├── hyp.scratch_VEDAI.yaml
│ ├── multispectral/
│ │ ├── CVC14.yaml
│ │ ├── FLIR-align-3class.yaml
│ │ ├── FLIR-align.yaml
│ │ ├── LLVIP.yaml
│ │ ├── VEDAI.yaml
│ │ └── kaist.yaml
│ ├── scripts/
│ │ ├── get_argoverse_hd.sh
│ │ ├── get_coco.sh
│ │ └── get_voc.sh
│ └── voc.yaml
├── descriptor/
│ ├── CFOG.py
│ ├── CFOG_matlab.p
│ ├── LSS.py
│ ├── denseLSS.m
│ └── mexCalcSsdescs1.mexw64
├── detect_twostream.py
├── evaluation_script/
│ ├── KAIST_annotation.json
│ ├── README.md
│ ├── __init__.py
│ ├── coco.py
│ ├── cocoeval.py
│ ├── evaluation_script.py
│ ├── null
│ └── state_of_arts/
│ ├── ARCNN_result.txt
│ ├── CIAN_result.txt
│ ├── MBNet_result.txt
│ ├── MLPD_result.json
│ ├── MLPD_result.txt
│ └── MSDS-RCNN_result.txt
├── global_var.py
├── gradcam_visual.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ ├── export.py
│ ├── gradcam.py
│ ├── hub/
│ │ ├── anchors.yaml
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov3-tiny.yaml
│ │ ├── yolov3.yaml
│ │ ├── yolov5-fpn.yaml
│ │ ├── yolov5-p2.yaml
│ │ ├── yolov5-p6.yaml
│ │ ├── yolov5-p7.yaml
│ │ ├── yolov5-panet.yaml
│ │ ├── yolov5l6.yaml
│ │ ├── yolov5m6.yaml
│ │ ├── yolov5s-transformer.yaml
│ │ ├── yolov5s6.yaml
│ │ └── yolov5x6.yaml
│ ├── transformer/
│ │ ├── yolov5_ResNet50_NiNfusion_FLIR.yaml
│ │ ├── yolov5_ResNet50_NiNfusion_kaist.yaml
│ │ ├── yolov5_ResNet50_Transfusion_FLIR.yaml
│ │ ├── yolov5_ResNet50_Transfusion_kaist.yaml
│ │ ├── yolov5_VGG16_NiNfusion_FLIR.yaml
│ │ ├── yolov5_VGG16_NiNfusion_kaist.yaml
│ │ ├── yolov5_VGG16_Transfusion_FLIR.yaml
│ │ ├── yolov5_VGG16_Transfusion_kaist.yaml
│ │ ├── yolov5l_Add_FLIR.yaml
│ │ ├── yolov5l_MobileViT_NiNfusion_FLIR.yaml
│ │ ├── yolov5l_NiNfusion_FLIR.yaml
│ │ ├── yolov5l_NiNfusion_LLVIP.yaml
│ │ ├── yolov5l_NiNfusion_VEDAI.yaml
│ │ ├── yolov5l_Transfusion_FLIR.yaml
│ │ ├── yolov5l_Transfusion_LLVIP.yaml
│ │ ├── yolov5l_Transfusion_VEDAI.yaml
│ │ ├── yolov5l_Transfusion_kaist.yaml
│ │ ├── yolov5m_Add_kaist.yaml
│ │ ├── yolov5m_NiNfusion_FLIR.yaml
│ │ ├── yolov5m_NiNfusion_kaist.yaml
│ │ ├── yolov5m_Transfusion_FLIR.yaml
│ │ ├── yolov5m_Transfusion_SeaDrone.yaml
│ │ ├── yolov5m_Transfusion_VEDAI.yaml
│ │ ├── yolov5m_Transfusion_kaist.yaml
│ │ ├── yolov5m_weightedAdd_kaist.yaml
│ │ ├── yolov5n_Add_kaist.yaml
│ │ ├── yolov5n_NiNfusion_FLIR.yaml
│ │ ├── yolov5n_Transfusion_FLIR.yaml
│ │ ├── yolov5n_Transfusion_kaist.yaml
│ │ ├── yolov5s_Add_kaist.yaml
│ │ ├── yolov5s_Transfusion_FLIR.yaml
│ │ └── yolov5s_Transfusion_kaist.yaml
│ ├── yolo.py
│ ├── yolo_test.py
│ ├── yolov5l.yaml
│ ├── yolov5m.yaml
│ ├── yolov5s.yaml
│ └── yolov5x.yaml
├── requirements.txt
├── test.py
├── train.py
└── utils/
├── __init__.py
├── activations.py
├── autoanchor.py
├── aws/
│ ├── __init__.py
│ ├── mime.sh
│ ├── resume.py
│ └── userdata.sh
├── confluence.py
├── datasets.py
├── flask_rest_api/
│ ├── example_request.py
│ └── restapi.py
├── general.py
├── google_app_engine/
│ ├── Dockerfile
│ ├── additional_requirements.txt
│ └── app.yaml
├── google_utils.py
├── gradcam.py
├── loss.py
├── metrics.py
├── plots.py
├── torch_utils.py
└── wandb_logging/
├── __init__.py
├── log_dataset.py
└── wandb_utils.py
SYMBOL INDEX (505 symbols across 31 files)
FILE: confluence.py
function assign_boxes_to_classes (line 15) | def assign_boxes_to_classes(bounding_boxes, classes, scores):
function normalise_coordinates (line 30) | def normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y):
function confluence_nms (line 41) | def confluence_nms(bounding_boxes,scores,classes,confluence_thr,gaussian...
function confluence (line 92) | def confluence(bounding_boxes,scores,classes,confluence_thr,gaussian,sco...
FILE: descriptor/CFOG.py
function denseCFOG (line 9) | def denseCFOG(image):
function denseCFOG2D (line 27) | def denseCFOG2D(image):
FILE: descriptor/LSS.py
function denseLSS (line 8) | def denseLSS(image):
function denseLSS2D (line 26) | def denseLSS2D(image):
function denseLSS_matlab (line 35) | def denseLSS_matlab(image):
function denseLSS2D_matlab (line 53) | def denseLSS2D_matlab(image):
FILE: detect_twostream.py
function detect (line 19) | def detect(opt):
FILE: evaluation_script/coco.py
function _isArrayLike (line 66) | def _isArrayLike(obj):
class COCO (line 70) | class COCO:
method __init__ (line 71) | def __init__(self, annotation_file=None):
method createIndex (line 90) | def createIndex(self):
method info (line 121) | def info(self):
method getAnnIds (line 129) | def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
method getCatIds (line 157) | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
method getImgIds (line 179) | def getImgIds(self, imgIds=[], catIds=[]):
method loadAnns (line 200) | def loadAnns(self, ids=[]):
method loadCats (line 211) | def loadCats(self, ids=[]):
method loadImgs (line 222) | def loadImgs(self, ids=[]):
method showAnns (line 233) | def showAnns(self, anns, draw_bbox=False):
method loadRes (line 305) | def loadRes(self, resFile):
method download (line 366) | def download(self, tarDir = None, imgIds = [] ):
method loadNumpyAnnotations (line 390) | def loadNumpyAnnotations(self, data):
method annToRLE (line 413) | def annToRLE(self, ann):
method annToMask (line 434) | def annToMask(self, ann):
FILE: evaluation_script/cocoeval.py
class COCOeval (line 10) | class COCOeval:
method __init__ (line 60) | def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'):
method _prepare (line 84) | def _prepare(self):
method evaluate (line 121) | def evaluate(self):
method computeIoU (line 163) | def computeIoU(self, imgId, catId):
method computeOks (line 192) | def computeOks(self, imgId, catId):
method evaluateImg (line 235) | def evaluateImg(self, imgId, catId, aRng, maxDet):
method accumulate (line 315) | def accumulate(self, p = None):
method summarize (line 422) | def summarize(self):
method __str__ (line 495) | def __str__(self):
class Params (line 498) | class Params:
method setDetParams (line 502) | def setDetParams(self):
method setKpParams (line 513) | def setKpParams(self):
method __init__ (line 525) | def __init__(self, iouType='segm'):
FILE: evaluation_script/evaluation_script.py
class KAISTPedEval (line 32) | class KAISTPedEval(COCOeval):
method __init__ (line 34) | def __init__(self, kaistGt=None, kaistDt=None, iouType='segm', method=...
method _prepare (line 46) | def _prepare(self, id_setup):
method evaluate (line 83) | def evaluate(self, id_setup):
method computeIoU (line 119) | def computeIoU(self, imgId, catId):
method iou (line 148) | def iou(self, dts, gts, pyiscrowd):
method evaluateImg (line 181) | def evaluateImg(self, imgId, catId, hRng, oRng, maxDet):
method accumulate (line 296) | def accumulate(self, p=None):
method draw_figure (line 398) | def draw_figure(ax, eval_results, methods, colors):
method summarize (line 432) | def summarize(self, id_setup, res_file=None):
class KAISTParams (line 478) | class KAISTParams(Params):
method setDetParams (line 481) | def setDetParams(self):
class KAIST (line 500) | class KAIST(COCO):
method txt2json (line 502) | def txt2json(self, txt):
method loadRes (line 523) | def loadRes(self, resFile):
function evaluate (line 546) | def evaluate(test_annotation_file: str, user_submission_file: str, phase...
function draw_all (line 649) | def draw_all(eval_results, filename='figure.jpg'):
FILE: global_var.py
function _init (line 7) | def _init(): # 初始化
function set_value (line 12) | def set_value(key, value):
function get_value (line 17) | def get_value(key):
FILE: gradcam_visual.py
function get_res_img2 (line 32) | def get_res_img2(heat, mask, res_img):
function get_res_img (line 40) | def get_res_img(bbox, mask, res_img):
function put_text_box (line 50) | def put_text_box(bbox, cls_name, res_img):
function concat_images (line 61) | def concat_images(images):
function main (line 71) | def main(img_vis_path, img_ir_path):
FILE: hubconf.py
function create (line 21) | def create(name, pretrained, channels, classes, autoshape, verbose):
function custom (line 59) | def custom(path_or_model='path/to/model.pt', autoshape=True, verbose=True):
function yolov5s (line 85) | def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, ver...
function yolov5m (line 90) | def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, ver...
function yolov5l (line 95) | def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, ver...
function yolov5x (line 100) | def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, ver...
function yolov5s6 (line 105) | def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, ve...
function yolov5m6 (line 110) | def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, ve...
function yolov5l6 (line 115) | def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, ve...
function yolov5x6 (line 120) | def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, ve...
FILE: models/common.py
function autopad (line 36) | def autopad(k, p=None): # kernel, padding
function DWConv (line 43) | def DWConv(c1, c2, k=1, s=1, act=True):
class Conv (line 48) | class Conv(nn.Module):
method __init__ (line 50) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 56) | def forward(self, x):
method fuseforward (line 59) | def fuseforward(self, x):
class TransformerLayer (line 63) | class TransformerLayer(nn.Module):
method __init__ (line 65) | def __init__(self, c, num_heads):
method forward (line 74) | def forward(self, x):
class TransformerBlock (line 80) | class TransformerBlock(nn.Module):
method __init__ (line 82) | def __init__(self, c1, c2, num_heads, num_layers):
method forward (line 91) | def forward(self, x):
class VGGblock (line 109) | class VGGblock(nn.Module):
method __init__ (line 110) | def __init__(self, num_convs, c1, c2):
method forward (line 125) | def forward(self, x):
class ResNetblock (line 131) | class ResNetblock(nn.Module):
method __init__ (line 134) | def __init__(self, c1, c2, stride=1):
method forward (line 149) | def forward(self, x):
class ResNetlayer (line 159) | class ResNetlayer(nn.Module):
method __init__ (line 162) | def __init__(self, c1, c2, stride=1, is_first=False, num_blocks=1):
method forward (line 178) | def forward(self, x):
class Bottleneck (line 184) | class Bottleneck(nn.Module):
method __init__ (line 186) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
method forward (line 193) | def forward(self, x):
class BottleneckCSP (line 197) | class BottleneckCSP(nn.Module):
method __init__ (line 199) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 210) | def forward(self, x):
class C3 (line 216) | class C3(nn.Module):
method __init__ (line 218) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 226) | def forward(self, x):
class C3TR (line 230) | class C3TR(C3):
method __init__ (line 232) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
class SPP (line 238) | class SPP(nn.Module):
method __init__ (line 240) | def __init__(self, c1, c2, k=(5, 9, 13)):
method forward (line 247) | def forward(self, x):
class SPPF (line 252) | class SPPF(nn.Module):
method __init__ (line 254) | def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
method forward (line 261) | def forward(self, x):
class Focus (line 270) | class Focus(nn.Module):
method __init__ (line 272) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 278) | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
class Contract (line 285) | class Contract(nn.Module):
method __init__ (line 287) | def __init__(self, gain=2):
method forward (line 291) | def forward(self, x):
class Expand (line 299) | class Expand(nn.Module):
method __init__ (line 301) | def __init__(self, gain=2):
method forward (line 305) | def forward(self, x):
class Concat (line 313) | class Concat(nn.Module):
method __init__ (line 315) | def __init__(self, dimension=1):
method forward (line 319) | def forward(self, x):
class Add (line 324) | class Add(nn.Module):
method __init__ (line 326) | def __init__(self, weight=0.5):
method forward (line 330) | def forward(self, x):
class Add2 (line 334) | class Add2(nn.Module):
method __init__ (line 336) | def __init__(self, c1, index):
method forward (line 340) | def forward(self, x):
class NiNfusion (line 348) | class NiNfusion(nn.Module):
method __init__ (line 349) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
method forward (line 356) | def forward(self, x):
class DMAF (line 363) | class DMAF(nn.Module):
method __init__ (line 364) | def __init__(self, c2):
method forward (line 368) | def forward(self, x):
class NMS (line 386) | class NMS(nn.Module):
method __init__ (line 392) | def __init__(self):
method forward (line 395) | def forward(self, x):
class autoShape (line 399) | class autoShape(nn.Module):
method __init__ (line 405) | def __init__(self, model):
method autoshape (line 409) | def autoshape(self):
method forward (line 414) | def forward(self, imgs, size=640, augment=False, profile=False):
class Detections (line 469) | class Detections:
method __init__ (line 471) | def __init__(self, imgs, pred, files, times=None, names=None, shape=No...
method display (line 487) | def display(self, pprint=False, show=False, save=False, crop=False, re...
method print (line 514) | def print(self):
method show (line 518) | def show(self):
method save (line 521) | def save(self, save_dir='runs/hub/exp'):
method crop (line 525) | def crop(self, save_dir='runs/hub/exp'):
method render (line 530) | def render(self):
method pandas (line 534) | def pandas(self):
method tolist (line 544) | def tolist(self):
method __len__ (line 552) | def __len__(self):
class Classify (line 556) | class Classify(nn.Module):
method __init__ (line 558) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, k...
method forward (line 564) | def forward(self, x):
class LearnableCoefficient (line 569) | class LearnableCoefficient(nn.Module):
method __init__ (line 570) | def __init__(self):
method forward (line 574) | def forward(self, x):
class LearnableWeights (line 579) | class LearnableWeights(nn.Module):
method __init__ (line 580) | def __init__(self):
method forward (line 585) | def forward(self, x1, x2):
class CrossAttention (line 590) | class CrossAttention(nn.Module):
method __init__ (line 591) | def __init__(self, d_model, d_k, d_v, h, attn_pdrop=.1, resid_pdrop=.1):
method init_weights (line 627) | def init_weights(self):
method forward (line 641) | def forward(self, x, attention_mask=None, attention_weights=None):
class CrossTransformerBlock (line 690) | class CrossTransformerBlock(nn.Module):
method __init__ (line 691) | def __init__(self, d_model, d_k, d_v, h, block_exp, attn_pdrop, resid_...
method forward (line 737) | def forward(self, x):
class TransformerFusionBlock (line 762) | class TransformerFusionBlock(nn.Module):
method __init__ (line 763) | def __init__(self, d_model, vert_anchors=16, horz_anchors=16, h=8, blo...
method _init_weights (line 800) | def _init_weights(module):
method forward (line 809) | def forward(self, x):
class AdaptivePool2d (line 868) | class AdaptivePool2d(nn.Module):
method __init__ (line 869) | def __init__(self, output_h, output_w, pool_type='avg'):
method forward (line 876) | def forward(self, x):
class SE_Block (line 893) | class SE_Block(nn.Module):
method __init__ (line 894) | def __init__(self, inchannel, ratio=16):
method forward (line 904) | def forward(self, x):
class Channel_Attention (line 913) | class Channel_Attention(nn.Module):
method __init__ (line 914) | def __init__(self, in_channels, reduction_ratio=16, pool_types=['avg',...
method forward (line 931) | def forward(self, x):
class Spatial_Attention (line 951) | class Spatial_Attention(nn.Module):
method __init__ (line 952) | def __init__(self, kernel_size=7):
method forward (line 959) | def forward(self, x):
class CBAM (line 967) | class CBAM(nn.Module):
method __init__ (line 968) | def __init__(self, in_channels, reduction_ratio=16, pool_types=['avg',...
method forward (line 977) | def forward(self, x):
FILE: models/experimental.py
class CrossConv (line 11) | class CrossConv(nn.Module):
method __init__ (line 13) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
method forward (line 21) | def forward(self, x):
class Sum (line 25) | class Sum(nn.Module):
method __init__ (line 27) | def __init__(self, n, weight=False): # n: number of inputs
method forward (line 34) | def forward(self, x):
class GhostConv (line 46) | class GhostConv(nn.Module):
method __init__ (line 48) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out,...
method forward (line 54) | def forward(self, x):
class GhostBottleneck (line 59) | class GhostBottleneck(nn.Module):
method __init__ (line 61) | def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
method forward (line 70) | def forward(self, x):
class MixConv2d (line 74) | class MixConv2d(nn.Module):
method __init__ (line 76) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
method forward (line 94) | def forward(self, x):
class Ensemble (line 98) | class Ensemble(nn.ModuleList):
method __init__ (line 100) | def __init__(self):
method forward (line 103) | def forward(self, x, augment=False):
function attempt_load (line 113) | def attempt_load(weights, map_location=None):
FILE: models/gradcam.py
function find_yolo_layer (line 6) | def find_yolo_layer(model, layer_name):
class YOLOV5GradCAM (line 24) | class YOLOV5GradCAM:
method __init__ (line 26) | def __init__(self, model, layer_name, img_size=(640, 640)):
method forward (line 47) | def forward(self, img_vis, img_ir, class_idx=True):
method __call__ (line 83) | def __call__(self, img_vis, img_ir):
FILE: models/yolo.py
class Detect (line 25) | class Detect(nn.Module):
method __init__ (line 29) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 41) | def forward(self, x):
method _make_grid (line 62) | def _make_grid(nx=20, ny=20):
class Model (line 67) | class Model(nn.Module):
method __init__ (line 68) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): ...
method forward (line 112) | def forward(self, x, augment=False, profile=False):
method forward_once (line 132) | def forward_once(self, x, profile=False):
method _initialize_biases (line 155) | def _initialize_biases(self, cf=None): # initialize biases into Detec...
method _print_biases (line 165) | def _print_biases(self):
method fuse (line 177) | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
method nms (line 187) | def nms(self, mode=True): # add or remove NMS module
method autoshape (line 201) | def autoshape(self): # add autoShape module
method info (line 207) | def info(self, verbose=False, img_size=640): # print model information
function parse_model (line 211) | def parse_model(d, ch): # model_dict, input_channels(3)
FILE: models/yolo_test.py
class Detect (line 26) | class Detect(nn.Module):
method __init__ (line 30) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 43) | def forward(self, x):
method _make_grid (line 68) | def _make_grid(nx=20, ny=20):
class Model (line 73) | class Model(nn.Module):
method __init__ (line 75) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): ...
method forward (line 115) | def forward(self, x, x2, augment=False, profile=False):
method forward_once (line 136) | def forward_once(self, x, x2, profile=False):
method _initialize_biases (line 165) | def _initialize_biases(self, cf=None): # initialize biases into Detec...
method _print_biases (line 175) | def _print_biases(self):
method fuse (line 182) | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
method nms (line 192) | def nms(self, mode=True): # add or remove NMS module
method autoshape (line 206) | def autoshape(self): # add autoShape module
method info (line 212) | def info(self, verbose=False, img_size=640): # print model information
function parse_model (line 216) | def parse_model(d, ch): # model_dict, input_channels(3)
FILE: test.py
function test (line 23) | def test(data,
FILE: train.py
function train_rgb_ir (line 42) | def train_rgb_ir(hyp, opt, device, tb_writer=None):
FILE: utils/activations.py
class SiLU (line 9) | class SiLU(nn.Module): # export-friendly version of nn.SiLU()
method forward (line 11) | def forward(x):
class Hardswish (line 15) | class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
method forward (line 17) | def forward(x):
class Mish (line 23) | class Mish(nn.Module):
method forward (line 25) | def forward(x):
class MemoryEfficientMish (line 29) | class MemoryEfficientMish(nn.Module):
class F (line 30) | class F(torch.autograd.Function):
method forward (line 32) | def forward(ctx, x):
method backward (line 37) | def backward(ctx, grad_output):
method forward (line 43) | def forward(self, x):
class FReLU (line 48) | class FReLU(nn.Module):
method __init__ (line 49) | def __init__(self, c1, k=3): # ch_in, kernel
method forward (line 54) | def forward(self, x):
class AconC (line 59) | class AconC(nn.Module):
method __init__ (line 65) | def __init__(self, c1):
method forward (line 71) | def forward(self, x):
class MetaAconC (line 76) | class MetaAconC(nn.Module):
method __init__ (line 82) | def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r
method forward (line 92) | def forward(self, x):
FILE: utils/autoanchor.py
function check_anchor_order (line 12) | def check_anchor_order(m):
function check_anchors (line 23) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
function check_anchors_rgb_ir (line 62) | def check_anchors_rgb_ir(dataset, model, thr=4.0, imgsz=640):
function kmean_anchors (line 103) | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0...
FILE: utils/confluence.py
function xywh2xyxy (line 6) | def xywh2xyxy(x):
function scale_coords_x (line 15) | def scale_coords_x(img1_shape, coords, img0_shape):
function clip_coords (line 27) | def clip_coords(boxes, img_shape):
function plot_one_box (line 34) | def plot_one_box(x, img, color=None, label=None, line_thickness=None):
function confluence_process (line 50) | def confluence_process(prediction, conf_thres=0.1, p_thres=0.6):
function confluence (line 109) | def confluence(prediction, class_num, p_thres=0.6):
function test (line 198) | def test():
FILE: utils/datasets.py
class RandomSampler (line 38) | class RandomSampler(torch.utils.data.sampler.RandomSampler):
method __init__ (line 40) | def __init__(self, data_source, replacement=False, num_samples=None):
method num_samples (line 58) | def num_samples(self):
method __iter__ (line 64) | def __iter__(self):
method __len__ (line 72) | def __len__(self):
function get_hash (line 82) | def get_hash(files):
function exif_size (line 87) | def exif_size(img):
function create_dataloader_rgb_ir (line 102) | def create_dataloader_rgb_ir(path1, path2, imgsz, batch_size, stride, o...
class InfiniteDataLoader (line 138) | class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
method __init__ (line 144) | def __init__(self, *args, **kwargs):
method __len__ (line 149) | def __len__(self):
method __iter__ (line 152) | def __iter__(self):
class _RepeatSampler (line 157) | class _RepeatSampler(object):
method __init__ (line 164) | def __init__(self, sampler):
method __iter__ (line 167) | def __iter__(self):
class LoadImages (line 172) | class LoadImages: # for inference
method __init__ (line 173) | def __init__(self, path, img_size=640, stride=32):
method __iter__ (line 201) | def __iter__(self):
method __next__ (line 205) | def __next__(self):
method new_video (line 243) | def new_video(self, path):
method __len__ (line 248) | def __len__(self):
class LoadWebcam (line 252) | class LoadWebcam: # for inference
method __init__ (line 253) | def __init__(self, pipe='0', img_size=640, stride=32):
method __iter__ (line 267) | def __iter__(self):
method __next__ (line 271) | def __next__(self):
method __len__ (line 306) | def __len__(self):
class LoadStreams (line 310) | class LoadStreams: # multiple IP or RTSP cameras
method __init__ (line 311) | def __init__(self, sources='streams.txt', img_size=640, stride=32):
method update (line 351) | def update(self, index, cap):
method __iter__ (line 364) | def __iter__(self):
method __next__ (line 368) | def __next__(self):
method __len__ (line 387) | def __len__(self):
function img2label_paths (line 391) | def img2label_paths(img_paths):
class LoadImagesAndLabels (line 404) | class LoadImagesAndLabels(Dataset): # for training/testing
method __init__ (line 405) | def __init__(self, path, img_size=640, batch_size=16, augment=False, h...
method cache_labels (line 512) | def cache_labels(self, path=Path('./labels.cache'), prefix=''):
method __len__ (line 567) | def __len__(self):
method __getitem__ (line 576) | def __getitem__(self, index):
method collate_fn (line 654) | def collate_fn(batch):
method collate_fn4 (line 661) | def collate_fn4(batch):
class LoadMultiModalImagesAndLabels (line 690) | class LoadMultiModalImagesAndLabels(Dataset): # for training/testing
method __init__ (line 694) | def __init__(self, path_rgb, path_ir, img_size=640, batch_size=16, aug...
method cache_labels (line 882) | def cache_labels(self, imgfiles, labelfiles, path=Path('./labels.cache...
method __len__ (line 939) | def __len__(self):
method __getitem__ (line 948) | def __getitem__(self, index):
method collate_fn (line 1027) | def collate_fn(batch):
method collate_fn4 (line 1034) | def collate_fn4(batch):
function shift_augment (line 1061) | def shift_augment(self, img):
function load_image (line 1080) | def load_image(self, index):
function load_image_rgb_ir (line 1097) | def load_image_rgb_ir(self, index):
function augment_hsv (line 1129) | def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
function hist_equalize (line 1143) | def hist_equalize(img, clahe=True, bgr=False):
function load_mosaic (line 1154) | def load_mosaic(self, index):
function load_mosaic_RGB_IR (line 1208) | def load_mosaic_RGB_IR(self, index1, index2):
function load_mosaic9 (line 1313) | def load_mosaic9(self, index):
function replicate (line 1387) | def replicate(img, labels):
function letterbox (line 1404) | def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=Tru...
function random_perspective (line 1447) | def random_perspective(img, targets=(), segments=(), degrees=10, transla...
function random_perspective_rgb_ir (line 1535) | def random_perspective_rgb_ir(img_rgb, img_ir, targets_rgb=(),targets_ir...
function box_candidates (line 1633) | def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e...
function cutout (line 1641) | def cutout(image, labels):
function create_folder (line 1687) | def create_folder(path='./new'):
function flatten_recursive (line 1694) | def flatten_recursive(path='../coco128'):
function extract_boxes (line 1702) | def extract_boxes(path='../coco128/'): # from utils.datasets import *; ...
function autosplit (line 1737) | def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only...
FILE: utils/flask_rest_api/restapi.py
function predict (line 17) | def predict():
FILE: utils/general.py
function set_logging (line 39) | def set_logging(rank=-1, verbose=True):
function init_seeds (line 45) | def init_seeds(seed=0, deterministic=False):
function get_latest_run (line 60) | def get_latest_run(search_dir='.'):
function isdocker (line 66) | def isdocker():
function emojis (line 71) | def emojis(str=''):
function file_size (line 76) | def file_size(file):
function check_online (line 81) | def check_online():
function check_git_status (line 91) | def check_git_status():
function check_requirements (line 113) | def check_requirements(requirements='requirements.txt', exclude=()):
function check_img_size (line 142) | def check_img_size(img_size, s=32):
function check_imshow (line 150) | def check_imshow():
function check_file (line 164) | def check_file(file):
function check_dataset (line 175) | def check_dataset(dict):
function check_version (line 198) | def check_version(current='0.0.0', minimum='0.0.0', name='version ', pin...
function download (line 210) | def download(url, dir='.', multi_thread=False):
function make_divisible (line 234) | def make_divisible(x, divisor):
function clean_str (line 239) | def clean_str(s):
function one_cycle (line 244) | def one_cycle(y1=0.0, y2=1.0, steps=100):
function colorstr (line 249) | def colorstr(*input):
function labels_to_class_weights (line 274) | def labels_to_class_weights(labels, nc=80):
function labels_to_image_weights (line 293) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
function coco80_to_coco91_class (line 301) | def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index...
function xyxy2xywh2 (line 312) | def xyxy2xywh2(x):
function xyxy2xywh (line 322) | def xyxy2xywh(x):
function xywh2xyxy (line 332) | def xywh2xyxy(x):
function xywhn2xyxy (line 342) | def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
function xyn2xy (line 352) | def xyn2xy(x, w=640, h=640, padw=0, padh=0):
function segment2box (line 360) | def segment2box(segment, width=640, height=640):
function segments2boxes (line 368) | def segments2boxes(segments):
function resample_segments (line 377) | def resample_segments(segments, n=1000):
function scale_coords (line 386) | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
function clip_coords (line 402) | def clip_coords(boxes, img_shape):
function bbox_iou (line 410) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal...
function box_iou (line 455) | def box_iou(box1, box2):
function wh_iou (line 480) | def wh_iou(wh1, wh2):
function python_nms (line 488) | def python_nms(dets, scores, iou_thresh):
function non_max_suppression (line 518) | def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, cla...
function strip_optimizer (line 610) | def strip_optimizer(f='best.pt', s=''): # from utils.general import *; ...
function print_mutation (line 626) | def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
function apply_classifier (line 657) | def apply_classifier(x, model, img, im0):
function save_one_box (line 692) | def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=F...
function increment_path (line 705) | def increment_path(path, exist_ok=False, sep='', mkdir=False):
FILE: utils/google_utils.py
function gsutil_getsize (line 13) | def gsutil_getsize(url=''):
function attempt_download (line 19) | def attempt_download(file, repo='ultralytics/yolov5'):
function gdrive_download (line 59) | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zi...
function get_token (line 94) | def get_token(cookie="./cookie"):
FILE: utils/gradcam.py
function preprocess_image (line 18) | def preprocess_image(img):
function show_cam_on_image (line 33) | def show_cam_on_image(img, mask, epoch, layer):
function calcGradCam (line 43) | def calcGradCam(imgpath, feature, epoch, layer):
FILE: utils/loss.py
function smooth_BCE (line 15) | def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues...
class BCEBlurWithLogitsLoss (line 20) | class BCEBlurWithLogitsLoss(nn.Module):
method __init__ (line 22) | def __init__(self, alpha=0.05):
method forward (line 27) | def forward(self, pred, true):
class FocalLoss (line 37) | class FocalLoss(nn.Module):
method __init__ (line 39) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
method forward (line 47) | def forward(self, pred, true):
class QFocalLoss (line 67) | class QFocalLoss(nn.Module):
method __init__ (line 69) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
method forward (line 77) | def forward(self, pred, true):
class VFLoss (line 94) | class VFLoss(nn.Module):
method __init__ (line 95) | def __init__(self, loss_fcn, gamma=2.0, alpha=0.25):
method forward (line 104) | def forward(self, pred, true):
class RankingLoss2 (line 121) | class RankingLoss2(nn.Module):
method __init__ (line 122) | def __init__(self, threshold):
method forward (line 127) | def forward(self, pred, true):
class RankingLoss (line 139) | class RankingLoss(nn.Module):
method __init__ (line 140) | def __init__(self, gamma):
method forward (line 145) | def forward(self, pred, true):
class SimLoss (line 177) | class SimLoss(nn.Module):
method __init__ (line 178) | def __init__(self, gamma):
method des_SSD (line 183) | def des_SSD(self, i, j, descriptor):
method des_NCC (line 202) | def des_NCC(self, i, j, descriptor):
method gradient_loss (line 220) | def gradient_loss(self, s, penalty='l2'):
method mse_loss (line 229) | def mse_loss(self, x, y):
method DSC (line 232) | def DSC(self, pred, target):
method gncc_loss (line 239) | def gncc_loss(self, I, J, eps=1e-5):
method compute_local_sums (line 252) | def compute_local_sums(self, I, J, filt, stride, padding, win):
method cc_loss (line 267) | def cc_loss(self, x, y):
method Get_Ja (line 277) | def Get_Ja(self, flow):
method NJ_loss (line 286) | def NJ_loss(self, ypred):
method lncc_loss (line 290) | def lncc_loss(self, i, j, win=[9, 9], eps=1e-5):
method forward (line 313) | def forward(self, reference, sensed_tran, sensed, reference_inv_tran, ...
class ComputeLoss (line 325) | class ComputeLoss:
method __init__ (line 327) | def __init__(self, model, autobalance=False):
method __call__ (line 354) | def __call__(self, p, targets): # predictions, targets, model
method build_targets (line 409) | def build_targets(self, p, targets):
FILE: utils/metrics.py
function fitness (line 12) | def fitness(x):
function ap_per_class (line 18) | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='....
function compute_ap (line 85) | def compute_ap(recall, precision):
class ConfusionMatrix (line 113) | class ConfusionMatrix:
method __init__ (line 115) | def __init__(self, nc, conf=0.25, iou_thres=0.45):
method process_batch (line 121) | def process_batch(self, detections, labels):
method matrix (line 161) | def matrix(self):
method plot (line 164) | def plot(self, save_dir='', names=()):
method print (line 183) | def print(self):
function plot_pr_curve (line 190) | def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
function plot_mc_curve (line 210) | def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Con...
FILE: utils/plots.py
class Colors (line 29) | class Colors:
method __init__ (line 31) | def __init__(self):
method __call__ (line 35) | def __call__(self, i, bgr=False):
method hex2rgb (line 40) | def hex2rgb(h): # rgb order (PIL)
function hist2d (line 47) | def hist2d(x, y, n=100):
function butter_lowpass_filtfilt (line 56) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
function plot_one_box (line 67) | def plot_one_box(x, im, color=None, label=None, line_thickness=3):
function plot_one_box_PIL (line 84) | def plot_one_box_PIL(box, im, color=None, label=None, line_thickness=None):
function plot_wh_methods (line 99) | def plot_wh_methods(): # from utils.plots import *; plot_wh_methods()
function output_to_target (line 119) | def output_to_target(output):
function plot_samples (line 128) | def plot_samples(batch_index, images, path, tcls, tbox, indices, anchors...
function plot_images (line 173) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N...
function plot_lr_scheduler (line 251) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
function plot_test_txt (line 268) | def plot_test_txt(): # from utils.plots import *; plot_test()
function plot_targets_txt (line 285) | def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
function plot_study_txt (line 298) | def plot_study_txt(path='', x=None): # from utils.plots import *; plot_...
function plot_labels (line 330) | def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
function plot_evolution (line 378) | def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.pl...
function profile_idetection (line 402) | def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
function plot_results_overlay (line 434) | def plot_results_overlay(start=0, stop=0): # from utils.plots import *;...
function plot_results (line 457) | def plot_results(file='path/to/results.csv', dir=''):
FILE: utils/torch_utils.py
function torch_distributed_zero_first (line 28) | def torch_distributed_zero_first(local_rank: int):
function init_torch_seeds (line 39) | def init_torch_seeds(seed=0):
function date_modified (line 48) | def date_modified(path=__file__):
function git_describe (line 54) | def git_describe(path=Path(__file__).parent): # path must be a directory
function select_device (line 63) | def select_device(device='', batch_size=None):
function time_synchronized (line 89) | def time_synchronized():
function profile (line 96) | def profile(x, ops, n=100, device=None):
function is_parallel (line 135) | def is_parallel(model):
function intersect_dicts (line 139) | def intersect_dicts(da, db, exclude=()):
function initialize_weights (line 144) | def initialize_weights(model):
function find_modules (line 157) | def find_modules(model, mclass=nn.Conv2d):
function sparsity (line 162) | def sparsity(model):
function prune (line 171) | def prune(model, amount=0.3):
function fuse_conv_and_bn (line 182) | def fuse_conv_and_bn(conv, bn):
function model_info (line 205) | def model_info(model, verbose=False, img_size=640):
function load_classifier (line 238) | def load_classifier(name='resnet101', n=2):
function scale_img (line 257) | def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,...
function copy_attr (line 270) | def copy_attr(a, b, include=(), exclude=()):
class ModelEMA (line 279) | class ModelEMA:
method __init__ (line 289) | def __init__(self, model, decay=0.9999, updates=0):
method update (line 299) | def update(self, model):
method update_attr (line 311) | def update_attr(self, model, include=(), exclude=('process_group', 're...
FILE: utils/wandb_logging/log_dataset.py
function create_dataset_artifact (line 10) | def create_dataset_artifact(opt):
FILE: utils/wandb_logging/wandb_utils.py
function remove_prefix (line 23) | def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
function check_wandb_config_file (line 27) | def check_wandb_config_file(data_config_file):
function get_run_info (line 34) | def get_run_info(run_path):
function check_wandb_resume (line 42) | def check_wandb_resume(opt):
function process_wandb_config_ddp_mode (line 56) | def process_wandb_config_ddp_mode(opt):
class WandbLogger (line 80) | class WandbLogger():
method __init__ (line 81) | def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
method check_and_upload_dataset (line 115) | def check_and_upload_dataset(self, opt):
method setup_training (line 126) | def setup_training(self, opt, data_dict):
method download_dataset_artifact (line 159) | def download_dataset_artifact(self, path, alias):
method download_model_artifact (line 167) | def download_model_artifact(self, opt):
method log_model (line 179) | def log_model(self, path, opt, epoch, fitness_score, best_model=False):
method log_dataset_artifact (line 193) | def log_dataset_artifact(self, data_file, single_cls, project, overwri...
method map_val_table_path (line 222) | def map_val_table_path(self):
method create_dataset_table (line 228) | def create_dataset_table(self, dataset, class_to_id, name='dataset'):
method log_training_progress (line 263) | def log_training_progress(self, predn, path, names):
method log (line 285) | def log(self, log_dict):
method end_epoch (line 290) | def end_epoch(self, best_result=False):
method finish_run (line 302) | def finish_run(self):
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Download .json
Condensed preview — 134 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (16,617K chars).
[
{
"path": ".idea/.gitignore",
"chars": 146,
"preview": "# 默认忽略的文件\n/shelf/\n/workspace.xml\n# 基于编辑器的 HTTP 客户端请求\n/httpRequests/\n# Datasource local storage ignored files\n/dataSource"
},
{
"path": ".idea/ICAFusion.iml",
"chars": 441,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"PYTHON_MODULE\" version=\"4\">\n <component name=\"NewModuleRootManager"
},
{
"path": ".idea/deployment.xml",
"chars": 419,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"PublishConfigData\" remoteFilesAllowedToD"
},
{
"path": ".idea/inspectionProfiles/Project_Default.xml",
"chars": 602,
"preview": "<component name=\"InspectionProjectProfileManager\">\n <profile version=\"1.0\">\n <option name=\"myName\" value=\"Project De"
},
{
"path": ".idea/inspectionProfiles/profiles_settings.xml",
"chars": 174,
"preview": "<component name=\"InspectionProjectProfileManager\">\n <settings>\n <option name=\"USE_PROJECT_PROFILE\" value=\"false\" />\n"
},
{
"path": ".idea/modules.xml",
"chars": 270,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectModuleManager\">\n <modules>\n "
},
{
"path": ".idea/vcs.xml",
"chars": 167,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"VcsDirectoryMappings\">\n <mapping dire"
},
{
"path": "LICENSE",
"chars": 34523,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C)"
},
{
"path": "README.md",
"chars": 4774,
"preview": "## <div align=\"center\">ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection</di"
},
{
"path": "confluence.py",
"chars": 8824,
"preview": "\"\"\"\nAuthor: Andrew Shepley\nContact: asheple2@une.edu.au\nSource: Confluence\nMethods\na) assign_boxes_to_classes\nb) normali"
},
{
"path": "data/GlobalWheat2020.yaml",
"chars": 2047,
"preview": "# Global Wheat 2020 dataset http://www.global-wheat.com/\n# Train command: python train.py --data GlobalWheat2020.yaml\n# "
},
{
"path": "data/VisDrone.yaml",
"chars": 2787,
"preview": "# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset\n# Train command: python train.py --data VisDrone"
},
{
"path": "data/argoverse_hd.yaml",
"chars": 848,
"preview": "# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/\n# Train command: pytho"
},
{
"path": "data/coco.yaml",
"chars": 1717,
"preview": "# COCO 2017 dataset http://cocodataset.org\n# Train command: python train.py --data coco.yaml\n# Default dataset location "
},
{
"path": "data/coco128.yaml",
"chars": 1543,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/hyp.finetune.yaml",
"chars": 854,
"preview": "# Hyperparameters for VOC finetuning\n# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epoch"
},
{
"path": "data/hyp.scratch.yaml",
"chars": 1576,
"preview": "# Hyperparameters for COCO training from scratch\n# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coc"
},
{
"path": "data/hyp.scratch_VEDAI.yaml",
"chars": 1576,
"preview": "# Hyperparameters for COCO training from scratch\n# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coc"
},
{
"path": "data/multispectral/CVC14.yaml",
"chars": 795,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/multispectral/FLIR-align-3class.yaml",
"chars": 873,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/multispectral/FLIR-align.yaml",
"chars": 820,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/multispectral/LLVIP.yaml",
"chars": 681,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/multispectral/VEDAI.yaml",
"chars": 900,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/multispectral/kaist.yaml",
"chars": 799,
"preview": "# COCO 2017 dataset http://cocodataset.org - first 128 training images\n# Train command: python train.py --data coco128.y"
},
{
"path": "data/scripts/get_argoverse_hd.sh",
"chars": 2017,
"preview": "#!/bin/bash\n# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/\n# Download"
},
{
"path": "data/scripts/get_coco.sh",
"chars": 962,
"preview": "#!/bin/bash\n# COCO 2017 dataset http://cocodataset.org\n# Download command: bash data/scripts/get_coco.sh\n# Train command"
},
{
"path": "data/scripts/get_voc.sh",
"chars": 4240,
"preview": "#!/bin/bash\n# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/\n# Download command: bash data/scripts/get_voc.s"
},
{
"path": "data/voc.yaml",
"chars": 737,
"preview": "# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/\n# Train command: python train.py --data voc.yaml\n# Default "
},
{
"path": "descriptor/CFOG.py",
"chars": 1216,
"preview": "import torch\r\nimport numpy as np\r\n#import matlab.engine\r\nimport torch.nn.functional as F\r\nfrom PIL import Image\r\nimport "
},
{
"path": "descriptor/LSS.py",
"chars": 1922,
"preview": "import torch\r\nimport numpy as np\r\n#import matlab.engine\r\n#eng = matlab.engine.start_matlab()\r\n#eng.cd('./descriptor',nar"
},
{
"path": "descriptor/denseLSS.m",
"chars": 1613,
"preview": "function des = denseLSS(img,desc_rad,nrad,nang);\r\n\r\n\r\nparms.patch_size=3;\r\nparms.desc_rad=desc_rad;\r\nparms.nrad=nrad;\r\np"
},
{
"path": "detect_twostream.py",
"chars": 11809,
"preview": "import argparse\nimport time\nfrom pathlib import Path\n\nimport cv2\nimport torch\nimport torch.backends.cudnn as cudnn\nimpor"
},
{
"path": "evaluation_script/KAIST_annotation.json",
"chars": 1595993,
"preview": "{\n \"info\": {\n \"dataset\": \"KAIST Multispectral Pedestrian Benchmark\",\n \"url\": \"https://soonminhwang.gith"
},
{
"path": "evaluation_script/README.md",
"chars": 1,
"preview": "\n"
},
{
"path": "evaluation_script/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "evaluation_script/coco.py",
"chars": 18821,
"preview": "__author__ = 'tylin'\n__version__ = '2.0'\n# Interface for accessing the Microsoft COCO dataset.\n\n# Microsoft COCO is a la"
},
{
"path": "evaluation_script/cocoeval.py",
"chars": 24144,
"preview": "__author__ = 'tsungyi'\n\nimport numpy as np\nimport datetime\nimport time\nfrom collections import defaultdict\n# from . impo"
},
{
"path": "evaluation_script/evaluation_script.py",
"chars": 26564,
"preview": "\"\"\"Evaluate performance on multispectral pedestrian detection benchmark\n\nThis script evalutes multispectral detection pe"
},
{
"path": "evaluation_script/null",
"chars": 0,
"preview": ""
},
{
"path": "evaluation_script/state_of_arts/ARCNN_result.txt",
"chars": 4305030,
"preview": "1,1.8509424,385.5025,34.075916,92.43564,0.004068177\n1,617.82874,213.01526,21.317932,74.87131,0.00073474366\n1,621.2999,85"
},
{
"path": "evaluation_script/state_of_arts/CIAN_result.txt",
"chars": 6424767,
"preview": "1,503.707,216.95691,19.568878,41.11145,35.526\n1,602.8636,218.46642,19.378601,33.72847,10.066\n1,-0.9738505,104.51169,29.5"
},
{
"path": "evaluation_script/state_of_arts/MBNet_result.txt",
"chars": 640996,
"preview": "1,502.3300,212.4550,19.9220,41.6480,0.03658492\n1,370.0030,202.7680,22.6000,44.4990,0.03585266\n1,123.9650,203.6010,23.823"
},
{
"path": "evaluation_script/state_of_arts/MLPD_result.json",
"chars": 1520653,
"preview": "[\n {\n \"image_id\": 0,\n \"category_id\": 1.0,\n \"bbox\": [\n 503.05118560791016,\n "
},
{
"path": "evaluation_script/state_of_arts/MLPD_result.txt",
"chars": 295136,
"preview": "1,503.0512,213.2522,18.0536,42.1411,0.12698865\n2,529.1219,224.2851,20.8807,47.9709,0.83398271\n3,564.2303,233.7404,21.863"
},
{
"path": "evaluation_script/state_of_arts/MSDS-RCNN_result.txt",
"chars": 668131,
"preview": "1,501.4891,211.0255,22.8042,49.9734,0.93672699\n1,370.7085,190.7871,27.5148,54.9644,0.00000000\n1,1.0000,381.9085,60.6944,"
},
{
"path": "global_var.py",
"chars": 382,
"preview": "# @Time : 2021/6/22 下午4:46 \n# @Author : Richard FANG\n# @File : global_var.py.py \n# @Software: PyCharm\n\n\ndef _init(): # "
},
{
"path": "gradcam_visual.py",
"chars": 7244,
"preview": "import os\nimport time\nimport argparse\nimport numpy as np\nfrom models.gradcam import YOLOV5GradCAM\nfrom models.yolo_v5_ob"
},
{
"path": "hubconf.py",
"chars": 5863,
"preview": "\"\"\"YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/\n\nUsage:\n import torch\n model = torch.hub."
},
{
"path": "models/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "models/common.py",
"chars": 40796,
"preview": "# YOLOv5 common modules\n\nimport math\nfrom copy import copy\nfrom pathlib import Path\nimport warnings\n\nimport cv2\nimport n"
},
{
"path": "models/experimental.py",
"chars": 5134,
"preview": "# YOLOv5 experimental modules\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom models.common import Conv, DW"
},
{
"path": "models/export.py",
"chars": 5964,
"preview": "\"\"\"Exports a YOLOv5 *.pt model to ONNX and TorchScript formats\n\nUsage:\n $ export PYTHONPATH=\"$PWD\" && python models/e"
},
{
"path": "models/gradcam.py",
"chars": 3235,
"preview": "import time\nimport torch\nimport torch.nn.functional as F\n\n\ndef find_yolo_layer(model, layer_name):\n \"\"\"Find yolov5 la"
},
{
"path": "models/hub/anchors.yaml",
"chars": 3356,
"preview": "# Default YOLOv5 anchors for COCO data\n\n\n# P5 --------------------------------------------------------------------------"
},
{
"path": "models/hub/yolov3-spp.yaml",
"chars": 1531,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov3-tiny.yaml",
"chars": 1196,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov3.yaml",
"chars": 1524,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-fpn.yaml",
"chars": 1245,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-p2.yaml",
"chars": 1738,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-p6.yaml",
"chars": 1809,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-p7.yaml",
"chars": 2232,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-panet.yaml",
"chars": 1459,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5l6.yaml",
"chars": 1977,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5m6.yaml",
"chars": 1979,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chan"
},
{
"path": "models/hub/yolov5s-transformer.yaml",
"chars": 1406,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chan"
},
{
"path": "models/hub/yolov5s6.yaml",
"chars": 1979,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chan"
},
{
"path": "models/hub/yolov5x6.yaml",
"chars": 1979,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer chan"
},
{
"path": "models/transformer/yolov5_ResNet50_NiNfusion_FLIR.yaml",
"chars": 2103,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_ResNet50_NiNfusion_kaist.yaml",
"chars": 2103,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_ResNet50_Transfusion_FLIR.yaml",
"chars": 2145,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_ResNet50_Transfusion_kaist.yaml",
"chars": 2145,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_VGG16_NiNfusion_FLIR.yaml",
"chars": 1949,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_VGG16_NiNfusion_kaist.yaml",
"chars": 1949,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_VGG16_Transfusion_FLIR.yaml",
"chars": 2007,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5_VGG16_Transfusion_kaist.yaml",
"chars": 2007,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_Add_FLIR.yaml",
"chars": 2152,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_MobileViT_NiNfusion_FLIR.yaml",
"chars": 2576,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_NiNfusion_FLIR.yaml",
"chars": 2179,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_NiNfusion_LLVIP.yaml",
"chars": 2179,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_NiNfusion_VEDAI.yaml",
"chars": 2179,
"preview": "# parameters\nnc: 9 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_Transfusion_FLIR.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_Transfusion_LLVIP.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_Transfusion_VEDAI.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 9 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5l_Transfusion_kaist.yaml",
"chars": 2148,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 1.00 # model depth multiple\nwidth_multiple: 1.00 # layer chann"
},
{
"path": "models/transformer/yolov5m_Add_kaist.yaml",
"chars": 2152,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_NiNfusion_FLIR.yaml",
"chars": 2179,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_NiNfusion_kaist.yaml",
"chars": 2086,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_Transfusion_FLIR.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_Transfusion_SeaDrone.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 7 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_Transfusion_VEDAI.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 9 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_Transfusion_kaist.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5m_weightedAdd_kaist.yaml",
"chars": 2182,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chann"
},
{
"path": "models/transformer/yolov5n_Add_kaist.yaml",
"chars": 2060,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer chann"
},
{
"path": "models/transformer/yolov5n_NiNfusion_FLIR.yaml",
"chars": 2087,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer chann"
},
{
"path": "models/transformer/yolov5n_Transfusion_FLIR.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer chann"
},
{
"path": "models/transformer/yolov5n_Transfusion_kaist.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer chann"
},
{
"path": "models/transformer/yolov5s_Add_kaist.yaml",
"chars": 2060,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chann"
},
{
"path": "models/transformer/yolov5s_Transfusion_FLIR.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 3 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chann"
},
{
"path": "models/transformer/yolov5s_Transfusion_kaist.yaml",
"chars": 2240,
"preview": "# parameters\nnc: 1 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chann"
},
{
"path": "models/yolo.py",
"chars": 13094,
"preview": "# YOLOv5 YOLO-specific modules\n\nimport argparse\nimport logging\nimport sys\nfrom copy import deepcopy\nfrom pathlib import "
},
{
"path": "models/yolo_test.py",
"chars": 13439,
"preview": "# YOLOv5 YOLO-specific modules\n\nimport argparse\nimport logging\nimport sys\nfrom copy import deepcopy\nfrom pathlib import "
},
{
"path": "models/yolov5l.yaml",
"chars": 1365,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/yolov5m.yaml",
"chars": 1367,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chan"
},
{
"path": "models/yolov5s.yaml",
"chars": 1367,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chan"
},
{
"path": "models/yolov5x.yaml",
"chars": 1367,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer chan"
},
{
"path": "requirements.txt",
"chars": 599,
"preview": "# pip install -r requirements.txt\n\n# base ----------------------------------------\nmatplotlib>=3.2.2\nnumpy>=1.18.5\nopenc"
},
{
"path": "test.py",
"chars": 21413,
"preview": "import argparse\nimport json\nimport os\nfrom pathlib import Path\nfrom threading import Thread\n\nimport numpy as np\nimport t"
},
{
"path": "train.py",
"chars": 36060,
"preview": "import argparse\nimport logging\nimport math\nimport os\nimport random\nimport time\nfrom copy import deepcopy\nfrom pathlib im"
},
{
"path": "utils/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "utils/activations.py",
"chars": 3722,
"preview": "# Activation functions\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# SiLU https://arxiv.org/pd"
},
{
"path": "utils/autoanchor.py",
"chars": 9327,
"preview": "# Auto-anchor utils\n\nimport numpy as np\nimport torch\nimport yaml\nfrom scipy.cluster.vq import kmeans\nfrom tqdm import tq"
},
{
"path": "utils/aws/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "utils/aws/mime.sh",
"chars": 780,
"preview": "# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/\n#"
},
{
"path": "utils/aws/resume.py",
"chars": 1095,
"preview": "# Resume all interrupted trainings in yolov5/ dir including DDP trainings\n# Usage: $ python utils/aws/resume.py\n\nimport "
},
{
"path": "utils/aws/userdata.sh",
"chars": 1237,
"preview": "#!/bin/bash\n# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html\n# This "
},
{
"path": "utils/confluence.py",
"chars": 7790,
"preview": "import cv2\nimport numpy as np\nimport torch\nimport random\n\ndef xywh2xyxy(x):\n # Transform box coordinates from [x, y, "
},
{
"path": "utils/datasets.py",
"chars": 76702,
"preview": "# Dataset utils and dataloaders\n\nimport glob\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport tim"
},
{
"path": "utils/flask_rest_api/example_request.py",
"chars": 299,
"preview": "\"\"\"Perform test request\"\"\"\nimport pprint\n\nimport requests\n\nDETECTION_URL = \"http://localhost:5000/v1/object-detection/yo"
},
{
"path": "utils/flask_rest_api/restapi.py",
"chars": 1078,
"preview": "\"\"\"\nRun a rest API exposing the yolov5s object detection model\n\"\"\"\nimport argparse\nimport io\n\nimport torch\nfrom PIL impo"
},
{
"path": "utils/general.py",
"chars": 29941,
"preview": "# YOLOv5 general utils\n\nimport glob\nimport logging\nimport math\nimport os\nimport platform\nimport random\nimport re\nimport "
},
{
"path": "utils/google_app_engine/Dockerfile",
"chars": 821,
"preview": "FROM gcr.io/google-appengine/python\n\n# Create a virtualenv for dependencies. This isolates these packages from\n# system-"
},
{
"path": "utils/google_app_engine/additional_requirements.txt",
"chars": 105,
"preview": "# add these requirements in your app on top of the existing ones\npip==18.1\nFlask==1.0.2\ngunicorn==19.9.0\n"
},
{
"path": "utils/google_app_engine/app.yaml",
"chars": 173,
"preview": "runtime: custom\nenv: flex\n\nservice: yolov5app\n\nliveness_check:\n initial_delay_sec: 600\n\nmanual_scaling:\n instances: 1\n"
},
{
"path": "utils/google_utils.py",
"chars": 5059,
"preview": "# Google utils: https://cloud.google.com/storage/docs/reference/libraries\n\nimport os\nimport platform\nimport subprocess\ni"
},
{
"path": "utils/gradcam.py",
"chars": 2185,
"preview": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Richard Fang\nimport torch\nfrom torch.autograd import Variable\nfro"
},
{
"path": "utils/loss.py",
"chars": 19380,
"preview": "# Loss functions\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom utils.general import bbox_iou\nfrom utils.t"
},
{
"path": "utils/metrics.py",
"chars": 9162,
"preview": "# Model validation metrics\n\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\nf"
},
{
"path": "utils/plots.py",
"chars": 19800,
"preview": "# Plotting utils\n\nimport glob\nimport math\nimport os\nimport random\nfrom copy import copy\nfrom pathlib import Path\n\nimport"
},
{
"path": "utils/torch_utils.py",
"chars": 12906,
"preview": "# YOLOv5 PyTorch utils\n\nimport datetime\nimport logging\nimport math\nimport os\nimport platform\nimport subprocess\nimport ti"
},
{
"path": "utils/wandb_logging/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "utils/wandb_logging/log_dataset.py",
"chars": 800,
"preview": "import argparse\n\nimport yaml\n\nfrom wandb_utils import WandbLogger\n\nWANDB_ARTIFACT_PREFIX = 'wandb-artifact://'\n\n\ndef cre"
},
{
"path": "utils/wandb_logging/wandb_utils.py",
"chars": 16221,
"preview": "import json\nimport sys\nfrom pathlib import Path\n\nimport torch\nimport yaml\nfrom tqdm import tqdm\n\nsys.path.append(str(Pat"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the chanchanchan97/ICAFusion GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 134 files (15.3 MB), approximately 4.0M tokens, and a symbol index with 505 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.