Repository: qqwweee/keras-yolo3 Branch: master Commit: e6598d13c703 Files: 23 Total size: 91.9 KB Directory structure: gitextract__bvuk8l3/ ├── .gitignore ├── LICENSE ├── README.md ├── coco_annotation.py ├── convert.py ├── darknet53.cfg ├── font/ │ ├── FiraMono-Medium.otf │ └── SIL Open Font License.txt ├── kmeans.py ├── model_data/ │ ├── coco_classes.txt │ ├── tiny_yolo_anchors.txt │ ├── voc_classes.txt │ └── yolo_anchors.txt ├── train.py ├── train_bottleneck.py ├── voc_annotation.py ├── yolo.py ├── yolo3/ │ ├── __init__.py │ ├── model.py │ └── utils.py ├── yolo_video.py ├── yolov3-tiny.cfg └── yolov3.cfg ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.jpg *.png *.weights *.h5 logs/ *_test.py # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2018 qqwweee Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # keras-yolo3 [![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE) ## Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K). --- ## Quick Start 1. Download YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/). 2. Convert the Darknet YOLO model to a Keras model. 3. Run YOLO detection. ``` wget https://pjreddie.com/media/files/yolov3.weights python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5 python yolo_video.py [OPTIONS...] --image, for image detection mode, OR python yolo_video.py [video_path] [output_path (optional)] ``` For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with `--model model_file` and `--anchors anchor_file`. ### Usage Use --help to see usage of yolo_video.py: ``` usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS] [--classes CLASSES] [--gpu_num GPU_NUM] [--image] [--input] [--output] positional arguments: --input Video input path --output Video output path optional arguments: -h, --help show this help message and exit --model MODEL path to model weight file, default model_data/yolo.h5 --anchors ANCHORS path to anchor definitions, default model_data/yolo_anchors.txt --classes CLASSES path to class definitions, default model_data/coco_classes.txt --gpu_num GPU_NUM Number of GPU to use, default 1 --image Image detection mode, will ignore all positional arguments ``` --- 4. MultiGPU usage: use `--gpu_num N` to use N GPUs. It is passed to the [Keras multi_gpu_model()](https://keras.io/utils/#multi_gpu_model). ## Training 1. Generate your own annotation file and class names file. One row for one image; Row format: `image_file_path box1 box2 ... boxN`; Box format: `x_min,y_min,x_max,y_max,class_id` (no space). For VOC dataset, try `python voc_annotation.py` Here is an example: ``` path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3 path/to/img2.jpg 120,300,250,600,2 ... ``` 2. Make sure you have run `python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5` The file model_data/yolo_weights.h5 is used to load pretrained weights. 3. Modify train.py and start training. `python train.py` Use your trained weights or checkpoint weights with command line option `--model model_file` when using yolo_video.py Remember to modify class path or anchor path, with `--classes class_file` and `--anchors anchor_file`. If you want to use original pretrained weights for YOLOv3: 1. `wget https://pjreddie.com/media/files/darknet53.conv.74` 2. rename it as darknet53.weights 3. `python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5` 4. use model_data/darknet53_weights.h5 in train.py --- ## Some issues to know 1. The test environment is - Python 3.5.2 - Keras 2.1.5 - tensorflow 1.6.0 2. Default anchors are used. If you use your own anchors, probably some changes are needed. 3. The inference result is not totally the same as Darknet but the difference is small. 4. The speed is slower than Darknet. Replacing PIL with opencv may help a little. 5. Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning. 6. The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed. 7. For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See [this](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) for more information on bottleneck features. ================================================ FILE: coco_annotation.py ================================================ import json from collections import defaultdict name_box_id = defaultdict(list) id_name = dict() f = open( "mscoco2017/annotations/instances_train2017.json", encoding='utf-8') data = json.load(f) annotations = data['annotations'] for ant in annotations: id = ant['image_id'] name = 'mscoco2017/train2017/%012d.jpg' % id cat = ant['category_id'] if cat >= 1 and cat <= 11: cat = cat - 1 elif cat >= 13 and cat <= 25: cat = cat - 2 elif cat >= 27 and cat <= 28: cat = cat - 3 elif cat >= 31 and cat <= 44: cat = cat - 5 elif cat >= 46 and cat <= 65: cat = cat - 6 elif cat == 67: cat = cat - 7 elif cat == 70: cat = cat - 9 elif cat >= 72 and cat <= 82: cat = cat - 10 elif cat >= 84 and cat <= 90: cat = cat - 11 name_box_id[name].append([ant['bbox'], cat]) f = open('train.txt', 'w') for key in name_box_id.keys(): f.write(key) box_infos = name_box_id[key] for info in box_infos: x_min = int(info[0][0]) y_min = int(info[0][1]) x_max = x_min + int(info[0][2]) y_max = y_min + int(info[0][3]) box_info = " %d,%d,%d,%d,%d" % ( x_min, y_min, x_max, y_max, int(info[1])) f.write(box_info) f.write('\n') f.close() ================================================ FILE: convert.py ================================================ #! /usr/bin/env python """ Reads Darknet config and weights and creates Keras model with TF backend. """ import argparse import configparser import io import os from collections import defaultdict import numpy as np from keras import backend as K from keras.layers import (Conv2D, Input, ZeroPadding2D, Add, UpSampling2D, MaxPooling2D, Concatenate) from keras.layers.advanced_activations import LeakyReLU from keras.layers.normalization import BatchNormalization from keras.models import Model from keras.regularizers import l2 from keras.utils.vis_utils import plot_model as plot parser = argparse.ArgumentParser(description='Darknet To Keras Converter.') parser.add_argument('config_path', help='Path to Darknet cfg file.') parser.add_argument('weights_path', help='Path to Darknet weights file.') parser.add_argument('output_path', help='Path to output Keras model file.') parser.add_argument( '-p', '--plot_model', help='Plot generated Keras model and save as image.', action='store_true') parser.add_argument( '-w', '--weights_only', help='Save as Keras weights file instead of model file.', action='store_true') def unique_config_sections(config_file): """Convert all config sections to have unique names. Adds unique suffixes to config sections for compability with configparser. """ section_counters = defaultdict(int) output_stream = io.StringIO() with open(config_file) as fin: for line in fin: if line.startswith('['): section = line.strip().strip('[]') _section = section + '_' + str(section_counters[section]) section_counters[section] += 1 line = line.replace(section, _section) output_stream.write(line) output_stream.seek(0) return output_stream # %% def _main(args): config_path = os.path.expanduser(args.config_path) weights_path = os.path.expanduser(args.weights_path) assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format( config_path) assert weights_path.endswith( '.weights'), '{} is not a .weights file'.format(weights_path) output_path = os.path.expanduser(args.output_path) assert output_path.endswith( '.h5'), 'output path {} is not a .h5 file'.format(output_path) output_root = os.path.splitext(output_path)[0] # Load weights and config. print('Loading weights.') weights_file = open(weights_path, 'rb') major, minor, revision = np.ndarray( shape=(3, ), dtype='int32', buffer=weights_file.read(12)) if (major*10+minor)>=2 and major<1000 and minor<1000: seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8)) else: seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4)) print('Weights Header: ', major, minor, revision, seen) print('Parsing Darknet config.') unique_config_file = unique_config_sections(config_path) cfg_parser = configparser.ConfigParser() cfg_parser.read_file(unique_config_file) print('Creating Keras model.') input_layer = Input(shape=(None, None, 3)) prev_layer = input_layer all_layers = [] weight_decay = float(cfg_parser['net_0']['decay'] ) if 'net_0' in cfg_parser.sections() else 5e-4 count = 0 out_index = [] for section in cfg_parser.sections(): print('Parsing section {}'.format(section)) if section.startswith('convolutional'): filters = int(cfg_parser[section]['filters']) size = int(cfg_parser[section]['size']) stride = int(cfg_parser[section]['stride']) pad = int(cfg_parser[section]['pad']) activation = cfg_parser[section]['activation'] batch_normalize = 'batch_normalize' in cfg_parser[section] padding = 'same' if pad == 1 and stride == 1 else 'valid' # Setting weights. # Darknet serializes convolutional weights as: # [bias/beta, [gamma, mean, variance], conv_weights] prev_layer_shape = K.int_shape(prev_layer) weights_shape = (size, size, prev_layer_shape[-1], filters) darknet_w_shape = (filters, weights_shape[2], size, size) weights_size = np.product(weights_shape) print('conv2d', 'bn' if batch_normalize else ' ', activation, weights_shape) conv_bias = np.ndarray( shape=(filters, ), dtype='float32', buffer=weights_file.read(filters * 4)) count += filters if batch_normalize: bn_weights = np.ndarray( shape=(3, filters), dtype='float32', buffer=weights_file.read(filters * 12)) count += 3 * filters bn_weight_list = [ bn_weights[0], # scale gamma conv_bias, # shift beta bn_weights[1], # running mean bn_weights[2] # running var ] conv_weights = np.ndarray( shape=darknet_w_shape, dtype='float32', buffer=weights_file.read(weights_size * 4)) count += weights_size # DarkNet conv_weights are serialized Caffe-style: # (out_dim, in_dim, height, width) # We would like to set these to Tensorflow order: # (height, width, in_dim, out_dim) conv_weights = np.transpose(conv_weights, [2, 3, 1, 0]) conv_weights = [conv_weights] if batch_normalize else [ conv_weights, conv_bias ] # Handle activation. act_fn = None if activation == 'leaky': pass # Add advanced activation later. elif activation != 'linear': raise ValueError( 'Unknown activation function `{}` in section {}'.format( activation, section)) # Create Conv2D layer if stride>1: # Darknet uses left and top padding instead of 'same' mode prev_layer = ZeroPadding2D(((1,0),(1,0)))(prev_layer) conv_layer = (Conv2D( filters, (size, size), strides=(stride, stride), kernel_regularizer=l2(weight_decay), use_bias=not batch_normalize, weights=conv_weights, activation=act_fn, padding=padding))(prev_layer) if batch_normalize: conv_layer = (BatchNormalization( weights=bn_weight_list))(conv_layer) prev_layer = conv_layer if activation == 'linear': all_layers.append(prev_layer) elif activation == 'leaky': act_layer = LeakyReLU(alpha=0.1)(prev_layer) prev_layer = act_layer all_layers.append(act_layer) elif section.startswith('route'): ids = [int(i) for i in cfg_parser[section]['layers'].split(',')] layers = [all_layers[i] for i in ids] if len(layers) > 1: print('Concatenating route layers:', layers) concatenate_layer = Concatenate()(layers) all_layers.append(concatenate_layer) prev_layer = concatenate_layer else: skip_layer = layers[0] # only one layer to route all_layers.append(skip_layer) prev_layer = skip_layer elif section.startswith('maxpool'): size = int(cfg_parser[section]['size']) stride = int(cfg_parser[section]['stride']) all_layers.append( MaxPooling2D( pool_size=(size, size), strides=(stride, stride), padding='same')(prev_layer)) prev_layer = all_layers[-1] elif section.startswith('shortcut'): index = int(cfg_parser[section]['from']) activation = cfg_parser[section]['activation'] assert activation == 'linear', 'Only linear activation supported.' all_layers.append(Add()([all_layers[index], prev_layer])) prev_layer = all_layers[-1] elif section.startswith('upsample'): stride = int(cfg_parser[section]['stride']) assert stride == 2, 'Only stride=2 supported.' all_layers.append(UpSampling2D(stride)(prev_layer)) prev_layer = all_layers[-1] elif section.startswith('yolo'): out_index.append(len(all_layers)-1) all_layers.append(None) prev_layer = all_layers[-1] elif section.startswith('net'): pass else: raise ValueError( 'Unsupported section header type: {}'.format(section)) # Create and save model. if len(out_index)==0: out_index.append(len(all_layers)-1) model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index]) print(model.summary()) if args.weights_only: model.save_weights('{}'.format(output_path)) print('Saved Keras weights to {}'.format(output_path)) else: model.save('{}'.format(output_path)) print('Saved Keras model to {}'.format(output_path)) # Check to see if all weights have been read. remaining_weights = len(weights_file.read()) / 4 weights_file.close() print('Read {} of {} from Darknet weights.'.format(count, count + remaining_weights)) if remaining_weights > 0: print('Warning: {} unused weights'.format(remaining_weights)) if args.plot_model: plot(model, to_file='{}.png'.format(output_root), show_shapes=True) print('Saved model plot to {}.png'.format(output_root)) if __name__ == '__main__': _main(parser.parse_args()) ================================================ FILE: darknet53.cfg ================================================ [net] # Testing batch=1 subdivisions=1 # Training # batch=64 # subdivisions=16 width=416 height=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 learning_rate=0.001 burn_in=1000 max_batches = 500200 policy=steps steps=400000,450000 scales=.1,.1 [convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky # Downsample [convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=32 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=1024 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear ================================================ FILE: font/SIL Open Font License.txt ================================================ Copyright (c) 2014, Mozilla Foundation https://mozilla.org/ with Reserved Font Name Fira Mono. 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The requirement for fonts to remain under this license does not apply to any document created using the Font Software. TERMINATION This license becomes null and void if any of the above conditions are not met. DISCLAIMER THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE. ================================================ FILE: kmeans.py ================================================ import numpy as np class YOLO_Kmeans: def __init__(self, cluster_number, filename): self.cluster_number = cluster_number self.filename = "2012_train.txt" def iou(self, boxes, clusters): # 1 box -> k clusters n = boxes.shape[0] k = self.cluster_number box_area = boxes[:, 0] * boxes[:, 1] box_area = box_area.repeat(k) box_area = np.reshape(box_area, (n, k)) cluster_area = clusters[:, 0] * clusters[:, 1] cluster_area = np.tile(cluster_area, [1, n]) cluster_area = np.reshape(cluster_area, (n, k)) box_w_matrix = np.reshape(boxes[:, 0].repeat(k), (n, k)) cluster_w_matrix = np.reshape(np.tile(clusters[:, 0], (1, n)), (n, k)) min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix) box_h_matrix = np.reshape(boxes[:, 1].repeat(k), (n, k)) cluster_h_matrix = np.reshape(np.tile(clusters[:, 1], (1, n)), (n, k)) min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix) inter_area = np.multiply(min_w_matrix, min_h_matrix) result = inter_area / (box_area + cluster_area - inter_area) return result def avg_iou(self, boxes, clusters): accuracy = np.mean([np.max(self.iou(boxes, clusters), axis=1)]) return accuracy def kmeans(self, boxes, k, dist=np.median): box_number = boxes.shape[0] distances = np.empty((box_number, k)) last_nearest = np.zeros((box_number,)) np.random.seed() clusters = boxes[np.random.choice( box_number, k, replace=False)] # init k clusters while True: distances = 1 - self.iou(boxes, clusters) current_nearest = np.argmin(distances, axis=1) if (last_nearest == current_nearest).all(): break # clusters won't change for cluster in range(k): clusters[cluster] = dist( # update clusters boxes[current_nearest == cluster], axis=0) last_nearest = current_nearest return clusters def result2txt(self, data): f = open("yolo_anchors.txt", 'w') row = np.shape(data)[0] for i in range(row): if i == 0: x_y = "%d,%d" % (data[i][0], data[i][1]) else: x_y = ", %d,%d" % (data[i][0], data[i][1]) f.write(x_y) f.close() def txt2boxes(self): f = open(self.filename, 'r') dataSet = [] for line in f: infos = line.split(" ") length = len(infos) for i in range(1, length): width = int(infos[i].split(",")[2]) - \ int(infos[i].split(",")[0]) height = int(infos[i].split(",")[3]) - \ int(infos[i].split(",")[1]) dataSet.append([width, height]) result = np.array(dataSet) f.close() return result def txt2clusters(self): all_boxes = self.txt2boxes() result = self.kmeans(all_boxes, k=self.cluster_number) result = result[np.lexsort(result.T[0, None])] self.result2txt(result) print("K anchors:\n {}".format(result)) print("Accuracy: {:.2f}%".format( self.avg_iou(all_boxes, result) * 100)) if __name__ == "__main__": cluster_number = 9 filename = "2012_train.txt" kmeans = YOLO_Kmeans(cluster_number, filename) kmeans.txt2clusters() ================================================ FILE: model_data/coco_classes.txt ================================================ person bicycle car motorbike aeroplane 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 sofa pottedplant bed diningtable toilet tvmonitor laptop mouse remote keyboard cell phone microwave oven toaster sink refrigerator book clock vase scissors teddy bear hair drier toothbrush ================================================ FILE: model_data/tiny_yolo_anchors.txt ================================================ 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 ================================================ FILE: model_data/voc_classes.txt ================================================ aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor ================================================ FILE: model_data/yolo_anchors.txt ================================================ 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 ================================================ FILE: train.py ================================================ """ Retrain the YOLO model for your own dataset. """ import numpy as np import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss from yolo3.utils import get_random_data def _main(): annotation_path = 'train.txt' log_dir = 'logs/000/' classes_path = 'model_data/voc_classes.txt' anchors_path = 'model_data/yolo_anchors.txt' class_names = get_classes(classes_path) num_classes = len(class_names) anchors = get_anchors(anchors_path) input_shape = (416,416) # multiple of 32, hw is_tiny_version = len(anchors)==6 # default setting if is_tiny_version: model = create_tiny_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5') else: model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=True, period=3) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1) val_split = 0.1 with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) np.random.seed(None) num_val = int(len(lines)*val_split) num_train = len(lines) - num_val # Train with frozen layers first, to get a stable loss. # Adjust num epochs to your dataset. This step is enough to obtain a not bad model. if True: model.compile(optimizer=Adam(lr=1e-3), loss={ # use custom yolo_loss Lambda layer. 'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = 32 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=50, initial_epoch=0, callbacks=[logging, checkpoint]) model.save_weights(log_dir + 'trained_weights_stage_1.h5') # Unfreeze and continue training, to fine-tune. # Train longer if the result is not good. if True: for i in range(len(model.layers)): model.layers[i].trainable = True model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change print('Unfreeze all of the layers.') batch_size = 32 # note that more GPU memory is required after unfreezing the body print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=100, initial_epoch=50, callbacks=[logging, checkpoint, reduce_lr, early_stopping]) model.save_weights(log_dir + 'trained_weights_final.h5') # Further training if needed. def get_classes(classes_path): '''loads the classes''' with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): '''loads the anchors from a file''' with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/yolo_weights.h5'): '''create the training model''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \ num_anchors//3, num_classes+5)) for l in range(3)] model_body = yolo_body(image_input, num_anchors//3, num_classes) print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze darknet53 body or freeze all but 3 output layers. num = (185, len(model_body.layers)-3)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5'): '''create the training model, for Tiny YOLOv3''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \ num_anchors//2, num_classes+5)) for l in range(2)] model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes) print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze the darknet body or freeze all but 2 output layers. num = (20, len(model_body.layers)-2)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): '''data generator for fit_generator''' n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i==0: np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape, random=True) image_data.append(image) box_data.append(box) i = (i+1) % n image_data = np.array(image_data) box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [image_data, *y_true], np.zeros(batch_size) def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes): n = len(annotation_lines) if n==0 or batch_size<=0: return None return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes) if __name__ == '__main__': _main() ================================================ FILE: train_bottleneck.py ================================================ """ Retrain the YOLO model for your own dataset. """ import os import numpy as np import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss from yolo3.utils import get_random_data def _main(): annotation_path = 'train.txt' log_dir = 'logs/000/' classes_path = 'model_data/coco_classes.txt' anchors_path = 'model_data/yolo_anchors.txt' class_names = get_classes(classes_path) num_classes = len(class_names) anchors = get_anchors(anchors_path) input_shape = (416,416) # multiple of 32, hw model, bottleneck_model, last_layer_model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=True, period=3) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1) val_split = 0.1 with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) np.random.seed(None) num_val = int(len(lines)*val_split) num_train = len(lines) - num_val # Train with frozen layers first, to get a stable loss. # Adjust num epochs to your dataset. This step is enough to obtain a not bad model. if True: # perform bottleneck training if not os.path.isfile("bottlenecks.npz"): print("calculating bottlenecks") batch_size=8 bottlenecks=bottleneck_model.predict_generator(data_generator_wrapper(lines, batch_size, input_shape, anchors, num_classes, random=False, verbose=True), steps=(len(lines)//batch_size)+1, max_queue_size=1) np.savez("bottlenecks.npz", bot0=bottlenecks[0], bot1=bottlenecks[1], bot2=bottlenecks[2]) # load bottleneck features from file dict_bot=np.load("bottlenecks.npz") bottlenecks_train=[dict_bot["bot0"][:num_train], dict_bot["bot1"][:num_train], dict_bot["bot2"][:num_train]] bottlenecks_val=[dict_bot["bot0"][num_train:], dict_bot["bot1"][num_train:], dict_bot["bot2"][num_train:]] # train last layers with fixed bottleneck features batch_size=8 print("Training last layers with bottleneck features") print('with {} samples, val on {} samples and batch size {}.'.format(num_train, num_val, batch_size)) last_layer_model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred}) last_layer_model.fit_generator(bottleneck_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, bottlenecks_train), steps_per_epoch=max(1, num_train//batch_size), validation_data=bottleneck_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, bottlenecks_val), validation_steps=max(1, num_val//batch_size), epochs=30, initial_epoch=0, max_queue_size=1) model.save_weights(log_dir + 'trained_weights_stage_0.h5') # train last layers with random augmented data model.compile(optimizer=Adam(lr=1e-3), loss={ # use custom yolo_loss Lambda layer. 'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = 16 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=50, initial_epoch=0, callbacks=[logging, checkpoint]) model.save_weights(log_dir + 'trained_weights_stage_1.h5') # Unfreeze and continue training, to fine-tune. # Train longer if the result is not good. if True: for i in range(len(model.layers)): model.layers[i].trainable = True model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change print('Unfreeze all of the layers.') batch_size = 4 # note that more GPU memory is required after unfreezing the body print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=100, initial_epoch=50, callbacks=[logging, checkpoint, reduce_lr, early_stopping]) model.save_weights(log_dir + 'trained_weights_final.h5') # Further training if needed. def get_classes(classes_path): '''loads the classes''' with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): '''loads the anchors from a file''' with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/yolo_weights.h5'): '''create the training model''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \ num_anchors//3, num_classes+5)) for l in range(3)] model_body = yolo_body(image_input, num_anchors//3, num_classes) print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze darknet53 body or freeze all but 3 output layers. num = (185, len(model_body.layers)-3)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) # get output of second last layers and create bottleneck model of it out1=model_body.layers[246].output out2=model_body.layers[247].output out3=model_body.layers[248].output bottleneck_model = Model([model_body.input, *y_true], [out1, out2, out3]) # create last layer model of last layers from yolo model in0 = Input(shape=bottleneck_model.output[0].shape[1:].as_list()) in1 = Input(shape=bottleneck_model.output[1].shape[1:].as_list()) in2 = Input(shape=bottleneck_model.output[2].shape[1:].as_list()) last_out0=model_body.layers[249](in0) last_out1=model_body.layers[250](in1) last_out2=model_body.layers[251](in2) model_last=Model(inputs=[in0, in1, in2], outputs=[last_out0, last_out1, last_out2]) model_loss_last =Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_last.output, *y_true]) last_layer_model = Model([in0,in1,in2, *y_true], model_loss_last) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model, bottleneck_model, last_layer_model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False): '''data generator for fit_generator''' n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i==0 and random: np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape, random=random) image_data.append(image) box_data.append(box) i = (i+1) % n image_data = np.array(image_data) if verbose: print("Progress: ",i,"/",n) box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [image_data, *y_true], np.zeros(batch_size) def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False): n = len(annotation_lines) if n==0 or batch_size<=0: return None return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random, verbose) def bottleneck_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, bottlenecks): n = len(annotation_lines) i = 0 while True: box_data = [] b0=np.zeros((batch_size,bottlenecks[0].shape[1],bottlenecks[0].shape[2],bottlenecks[0].shape[3])) b1=np.zeros((batch_size,bottlenecks[1].shape[1],bottlenecks[1].shape[2],bottlenecks[1].shape[3])) b2=np.zeros((batch_size,bottlenecks[2].shape[1],bottlenecks[2].shape[2],bottlenecks[2].shape[3])) for b in range(batch_size): _, box = get_random_data(annotation_lines[i], input_shape, random=False, proc_img=False) box_data.append(box) b0[b]=bottlenecks[0][i] b1[b]=bottlenecks[1][i] b2[b]=bottlenecks[2][i] i = (i+1) % n box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [b0, b1, b2, *y_true], np.zeros(batch_size) if __name__ == '__main__': _main() ================================================ FILE: voc_annotation.py ================================================ import xml.etree.ElementTree as ET from os import getcwd sets=[('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_annotation(year, image_id, list_file): in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) tree=ET.parse(in_file) root = tree.getroot() 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 = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) wd = getcwd() for year, image_set in sets: 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'%(wd, year, image_id)) convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close() ================================================ FILE: yolo.py ================================================ # -*- coding: utf-8 -*- """ Class definition of YOLO_v3 style detection model on image and video """ import colorsys import os from timeit import default_timer as timer import numpy as np from keras import backend as K from keras.models import load_model from keras.layers import Input from PIL import Image, ImageFont, ImageDraw from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body from yolo3.utils import letterbox_image import os from keras.utils import multi_gpu_model class YOLO(object): _defaults = { "model_path": 'model_data/yolo.h5', "anchors_path": 'model_data/yolo_anchors.txt', "classes_path": 'model_data/coco_classes.txt', "score" : 0.3, "iou" : 0.45, "model_image_size" : (416, 416), "gpu_num" : 1, } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" def __init__(self, **kwargs): self.__dict__.update(self._defaults) # set up default values self.__dict__.update(kwargs) # and update with user overrides self.class_names = self._get_class() self.anchors = self._get_anchors() self.sess = K.get_session() self.boxes, self.scores, self.classes = self.generate() def _get_class(self): classes_path = os.path.expanduser(self.classes_path) with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names def _get_anchors(self): anchors_path = os.path.expanduser(self.anchors_path) with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def generate(self): model_path = os.path.expanduser(self.model_path) assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.' # Load model, or construct model and load weights. num_anchors = len(self.anchors) num_classes = len(self.class_names) is_tiny_version = num_anchors==6 # default setting try: self.yolo_model = load_model(model_path, compile=False) except: self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \ if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes) self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match else: assert self.yolo_model.layers[-1].output_shape[-1] == \ num_anchors/len(self.yolo_model.output) * (num_classes + 5), \ 'Mismatch between model and given anchor and class sizes' print('{} model, anchors, and classes loaded.'.format(model_path)) # Generate colors for drawing bounding boxes. hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list( map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) np.random.seed(10101) # Fixed seed for consistent colors across runs. np.random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes. np.random.seed(None) # Reset seed to default. # Generate output tensor targets for filtered bounding boxes. self.input_image_shape = K.placeholder(shape=(2, )) if self.gpu_num>=2: self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num) boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors, len(self.class_names), self.input_image_shape, score_threshold=self.score, iou_threshold=self.iou) return boxes, scores, classes def detect_image(self, image): start = timer() if self.model_image_size != (None, None): assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required' assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required' boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size))) else: new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32)) boxed_image = letterbox_image(image, new_image_size) image_data = np.array(boxed_image, dtype='float32') print(image_data.shape) image_data /= 255. image_data = np.expand_dims(image_data, 0) # Add batch dimension. out_boxes, out_scores, out_classes = self.sess.run( [self.boxes, self.scores, self.classes], feed_dict={ self.yolo_model.input: image_data, self.input_image_shape: [image.size[1], image.size[0]], K.learning_phase(): 0 }) print('Found {} boxes for {}'.format(len(out_boxes), 'img')) font = ImageFont.truetype(font='font/FiraMono-Medium.otf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32')) thickness = (image.size[0] + image.size[1]) // 300 for i, c in reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] box = out_boxes[i] score = out_scores[i] label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) top, left, bottom, right = box top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32')) right = min(image.size[0], np.floor(right + 0.5).astype('int32')) print(label, (left, top), (right, bottom)) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) # My kingdom for a good redistributable image drawing library. for i in range(thickness): draw.rectangle( [left + i, top + i, right - i, bottom - i], outline=self.colors[c]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) draw.text(text_origin, label, fill=(0, 0, 0), font=font) del draw end = timer() print(end - start) return image def close_session(self): self.sess.close() def detect_video(yolo, video_path, output_path=""): import cv2 vid = cv2.VideoCapture(video_path) if not vid.isOpened(): raise IOError("Couldn't open webcam or video") video_FourCC = int(vid.get(cv2.CAP_PROP_FOURCC)) video_fps = vid.get(cv2.CAP_PROP_FPS) video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))) isOutput = True if output_path != "" else False if isOutput: print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size)) out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size) accum_time = 0 curr_fps = 0 fps = "FPS: ??" prev_time = timer() while True: return_value, frame = vid.read() image = Image.fromarray(frame) image = yolo.detect_image(image) result = np.asarray(image) curr_time = timer() exec_time = curr_time - prev_time prev_time = curr_time accum_time = accum_time + exec_time curr_fps = curr_fps + 1 if accum_time > 1: accum_time = accum_time - 1 fps = "FPS: " + str(curr_fps) curr_fps = 0 cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.50, color=(255, 0, 0), thickness=2) cv2.namedWindow("result", cv2.WINDOW_NORMAL) cv2.imshow("result", result) if isOutput: out.write(result) if cv2.waitKey(1) & 0xFF == ord('q'): break yolo.close_session() ================================================ FILE: yolo3/__init__.py ================================================ ================================================ FILE: yolo3/model.py ================================================ """YOLO_v3 Model Defined in Keras.""" from functools import wraps import numpy as np import tensorflow as tf from keras import backend as K from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.normalization import BatchNormalization from keras.models import Model from keras.regularizers import l2 from yolo3.utils import compose @wraps(Conv2D) def DarknetConv2D(*args, **kwargs): """Wrapper to set Darknet parameters for Convolution2D.""" darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)} darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2,2) else 'same' darknet_conv_kwargs.update(kwargs) return Conv2D(*args, **darknet_conv_kwargs) def DarknetConv2D_BN_Leaky(*args, **kwargs): """Darknet Convolution2D followed by BatchNormalization and LeakyReLU.""" no_bias_kwargs = {'use_bias': False} no_bias_kwargs.update(kwargs) return compose( DarknetConv2D(*args, **no_bias_kwargs), BatchNormalization(), LeakyReLU(alpha=0.1)) def resblock_body(x, num_filters, num_blocks): '''A series of resblocks starting with a downsampling Convolution2D''' # Darknet uses left and top padding instead of 'same' mode x = ZeroPadding2D(((1,0),(1,0)))(x) x = DarknetConv2D_BN_Leaky(num_filters, (3,3), strides=(2,2))(x) for i in range(num_blocks): y = compose( DarknetConv2D_BN_Leaky(num_filters//2, (1,1)), DarknetConv2D_BN_Leaky(num_filters, (3,3)))(x) x = Add()([x,y]) return x def darknet_body(x): '''Darknent body having 52 Convolution2D layers''' x = DarknetConv2D_BN_Leaky(32, (3,3))(x) x = resblock_body(x, 64, 1) x = resblock_body(x, 128, 2) x = resblock_body(x, 256, 8) x = resblock_body(x, 512, 8) x = resblock_body(x, 1024, 4) return x def make_last_layers(x, num_filters, out_filters): '''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer''' x = compose( DarknetConv2D_BN_Leaky(num_filters, (1,1)), DarknetConv2D_BN_Leaky(num_filters*2, (3,3)), DarknetConv2D_BN_Leaky(num_filters, (1,1)), DarknetConv2D_BN_Leaky(num_filters*2, (3,3)), DarknetConv2D_BN_Leaky(num_filters, (1,1)))(x) y = compose( DarknetConv2D_BN_Leaky(num_filters*2, (3,3)), DarknetConv2D(out_filters, (1,1)))(x) return x, y def yolo_body(inputs, num_anchors, num_classes): """Create YOLO_V3 model CNN body in Keras.""" darknet = Model(inputs, darknet_body(inputs)) x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5)) x = compose( DarknetConv2D_BN_Leaky(256, (1,1)), UpSampling2D(2))(x) x = Concatenate()([x,darknet.layers[152].output]) x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5)) x = compose( DarknetConv2D_BN_Leaky(128, (1,1)), UpSampling2D(2))(x) x = Concatenate()([x,darknet.layers[92].output]) x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5)) return Model(inputs, [y1,y2,y3]) def tiny_yolo_body(inputs, num_anchors, num_classes): '''Create Tiny YOLO_v3 model CNN body in keras.''' x1 = compose( DarknetConv2D_BN_Leaky(16, (3,3)), MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), DarknetConv2D_BN_Leaky(32, (3,3)), MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), DarknetConv2D_BN_Leaky(64, (3,3)), MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), DarknetConv2D_BN_Leaky(128, (3,3)), MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), DarknetConv2D_BN_Leaky(256, (3,3)))(inputs) x2 = compose( MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), DarknetConv2D_BN_Leaky(512, (3,3)), MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'), DarknetConv2D_BN_Leaky(1024, (3,3)), DarknetConv2D_BN_Leaky(256, (1,1)))(x1) y1 = compose( DarknetConv2D_BN_Leaky(512, (3,3)), DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2) x2 = compose( DarknetConv2D_BN_Leaky(128, (1,1)), UpSampling2D(2))(x2) y2 = compose( Concatenate(), DarknetConv2D_BN_Leaky(256, (3,3)), DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1]) return Model(inputs, [y1,y2]) def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False): """Convert final layer features to bounding box parameters.""" num_anchors = len(anchors) # Reshape to batch, height, width, num_anchors, box_params. anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2]) grid_shape = K.shape(feats)[1:3] # height, width grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], 1, 1]) grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, 1, 1]) grid = K.concatenate([grid_x, grid_y]) grid = K.cast(grid, K.dtype(feats)) feats = K.reshape( feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5]) # Adjust preditions to each spatial grid point and anchor size. box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats)) box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats)) box_confidence = K.sigmoid(feats[..., 4:5]) box_class_probs = K.sigmoid(feats[..., 5:]) if calc_loss == True: return grid, feats, box_xy, box_wh return box_xy, box_wh, box_confidence, box_class_probs def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape): '''Get corrected boxes''' box_yx = box_xy[..., ::-1] box_hw = box_wh[..., ::-1] input_shape = K.cast(input_shape, K.dtype(box_yx)) image_shape = K.cast(image_shape, K.dtype(box_yx)) new_shape = K.round(image_shape * K.min(input_shape/image_shape)) offset = (input_shape-new_shape)/2./input_shape scale = input_shape/new_shape box_yx = (box_yx - offset) * scale box_hw *= scale box_mins = box_yx - (box_hw / 2.) box_maxes = box_yx + (box_hw / 2.) boxes = K.concatenate([ box_mins[..., 0:1], # y_min box_mins[..., 1:2], # x_min box_maxes[..., 0:1], # y_max box_maxes[..., 1:2] # x_max ]) # Scale boxes back to original image shape. boxes *= K.concatenate([image_shape, image_shape]) return boxes def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape): '''Process Conv layer output''' box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats, anchors, num_classes, input_shape) boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape) boxes = K.reshape(boxes, [-1, 4]) box_scores = box_confidence * box_class_probs box_scores = K.reshape(box_scores, [-1, num_classes]) return boxes, box_scores def yolo_eval(yolo_outputs, anchors, num_classes, image_shape, max_boxes=20, score_threshold=.6, iou_threshold=.5): """Evaluate YOLO model on given input and return filtered boxes.""" num_layers = len(yolo_outputs) anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] # default setting input_shape = K.shape(yolo_outputs[0])[1:3] * 32 boxes = [] box_scores = [] for l in range(num_layers): _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l], anchors[anchor_mask[l]], num_classes, input_shape, image_shape) boxes.append(_boxes) box_scores.append(_box_scores) boxes = K.concatenate(boxes, axis=0) box_scores = K.concatenate(box_scores, axis=0) mask = box_scores >= score_threshold max_boxes_tensor = K.constant(max_boxes, dtype='int32') boxes_ = [] scores_ = [] classes_ = [] for c in range(num_classes): # TODO: use keras backend instead of tf. class_boxes = tf.boolean_mask(boxes, mask[:, c]) class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c]) nms_index = tf.image.non_max_suppression( class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold) class_boxes = K.gather(class_boxes, nms_index) class_box_scores = K.gather(class_box_scores, nms_index) classes = K.ones_like(class_box_scores, 'int32') * c boxes_.append(class_boxes) scores_.append(class_box_scores) classes_.append(classes) boxes_ = K.concatenate(boxes_, axis=0) scores_ = K.concatenate(scores_, axis=0) classes_ = K.concatenate(classes_, axis=0) return boxes_, scores_, classes_ def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes): '''Preprocess true boxes to training input format Parameters ---------- true_boxes: array, shape=(m, T, 5) Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape. input_shape: array-like, hw, multiples of 32 anchors: array, shape=(N, 2), wh num_classes: integer Returns ------- y_true: list of array, shape like yolo_outputs, xywh are reletive value ''' assert (true_boxes[..., 4]0 for b in range(m): # Discard zero rows. wh = boxes_wh[b, valid_mask[b]] if len(wh)==0: continue # Expand dim to apply broadcasting. wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) # Find best anchor for each true box best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32') k = anchor_mask[l].index(n) c = true_boxes[b,t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5+c] = 1 return y_true def box_iou(b1, b2): '''Return iou tensor Parameters ---------- b1: tensor, shape=(i1,...,iN, 4), xywh b2: tensor, shape=(j, 4), xywh Returns ------- iou: tensor, shape=(i1,...,iN, j) ''' # Expand dim to apply broadcasting. b1 = K.expand_dims(b1, -2) b1_xy = b1[..., :2] b1_wh = b1[..., 2:4] b1_wh_half = b1_wh/2. b1_mins = b1_xy - b1_wh_half b1_maxes = b1_xy + b1_wh_half # Expand dim to apply broadcasting. b2 = K.expand_dims(b2, 0) b2_xy = b2[..., :2] b2_wh = b2[..., 2:4] b2_wh_half = b2_wh/2. b2_mins = b2_xy - b2_wh_half b2_maxes = b2_xy + b2_wh_half intersect_mins = K.maximum(b1_mins, b2_mins) intersect_maxes = K.minimum(b1_maxes, b2_maxes) intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] b1_area = b1_wh[..., 0] * b1_wh[..., 1] b2_area = b2_wh[..., 0] * b2_wh[..., 1] iou = intersect_area / (b1_area + b2_area - intersect_area) return iou def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False): '''Return yolo_loss tensor Parameters ---------- yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body y_true: list of array, the output of preprocess_true_boxes anchors: array, shape=(N, 2), wh num_classes: integer ignore_thresh: float, the iou threshold whether to ignore object confidence loss Returns ------- loss: tensor, shape=(1,) ''' num_layers = len(anchors)//3 # default setting yolo_outputs = args[:num_layers] y_true = args[num_layers:] anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0])) grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)] loss = 0 m = K.shape(yolo_outputs[0])[0] # batch size, tensor mf = K.cast(m, K.dtype(yolo_outputs[0])) for l in range(num_layers): object_mask = y_true[l][..., 4:5] true_class_probs = y_true[l][..., 5:] grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l], anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True) pred_box = K.concatenate([pred_xy, pred_wh]) # Darknet raw box to calculate loss. raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1]) raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4] # Find ignore mask, iterate over each of batch. ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True) object_mask_bool = K.cast(object_mask, 'bool') def loop_body(b, ignore_mask): true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0]) iou = box_iou(pred_box[b], true_box) best_iou = K.max(iou, axis=-1) ignore_mask = ignore_mask.write(b, K.cast(best_iou0: np.random.shuffle(box) if len(box)>max_boxes: box = box[:max_boxes] box[:, [0,2]] = box[:, [0,2]]*scale + dx box[:, [1,3]] = box[:, [1,3]]*scale + dy box_data[:len(box)] = box return image_data, box_data # resize image new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter) scale = rand(.25, 2) if new_ar < 1: nh = int(scale*h) nw = int(nh*new_ar) else: nw = int(scale*w) nh = int(nw/new_ar) image = image.resize((nw,nh), Image.BICUBIC) # place image dx = int(rand(0, w-nw)) dy = int(rand(0, h-nh)) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image = new_image # flip image or not flip = rand()<.5 if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) # distort image hue = rand(-hue, hue) sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat) val = rand(1, val) if rand()<.5 else 1/rand(1, val) x = rgb_to_hsv(np.array(image)/255.) x[..., 0] += hue x[..., 0][x[..., 0]>1] -= 1 x[..., 0][x[..., 0]<0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x>1] = 1 x[x<0] = 0 image_data = hsv_to_rgb(x) # numpy array, 0 to 1 # correct boxes box_data = np.zeros((max_boxes,5)) if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy if flip: box[:, [0,2]] = w - box[:, [2,0]] box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box if len(box)>max_boxes: box = box[:max_boxes] box_data[:len(box)] = box return image_data, box_data ================================================ FILE: yolo_video.py ================================================ import sys import argparse from yolo import YOLO, detect_video from PIL import Image def detect_img(yolo): while True: img = input('Input image filename:') try: image = Image.open(img) except: print('Open Error! Try again!') continue else: r_image = yolo.detect_image(image) r_image.show() yolo.close_session() FLAGS = None if __name__ == '__main__': # class YOLO defines the default value, so suppress any default here parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS) ''' Command line options ''' parser.add_argument( '--model', type=str, help='path to model weight file, default ' + YOLO.get_defaults("model_path") ) parser.add_argument( '--anchors', type=str, help='path to anchor definitions, default ' + YOLO.get_defaults("anchors_path") ) parser.add_argument( '--classes', type=str, help='path to class definitions, default ' + YOLO.get_defaults("classes_path") ) parser.add_argument( '--gpu_num', type=int, help='Number of GPU to use, default ' + str(YOLO.get_defaults("gpu_num")) ) parser.add_argument( '--image', default=False, action="store_true", help='Image detection mode, will ignore all positional arguments' ) ''' Command line positional arguments -- for video detection mode ''' parser.add_argument( "--input", nargs='?', type=str,required=False,default='./path2your_video', help = "Video input path" ) parser.add_argument( "--output", nargs='?', type=str, default="", help = "[Optional] Video output path" ) FLAGS = parser.parse_args() if FLAGS.image: """ Image detection mode, disregard any remaining command line arguments """ print("Image detection mode") if "input" in FLAGS: print(" Ignoring remaining command line arguments: " + FLAGS.input + "," + FLAGS.output) detect_img(YOLO(**vars(FLAGS))) elif "input" in FLAGS: detect_video(YOLO(**vars(FLAGS)), FLAGS.input, FLAGS.output) else: print("Must specify at least video_input_path. See usage with --help.") ================================================ FILE: yolov3-tiny.cfg ================================================ [net] # Testing batch=1 subdivisions=1 # Training # batch=64 # subdivisions=2 width=416 height=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 learning_rate=0.001 burn_in=1000 max_batches = 500200 policy=steps steps=400000,450000 scales=.1,.1 [convolutional] batch_normalize=1 filters=16 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=2 [convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=2 [convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=2 [convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=2 [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=2 [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [maxpool] size=2 stride=1 [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky ########### [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=255 activation=linear [yolo] mask = 3,4,5 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=80 num=6 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1 [route] layers = -4 [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [upsample] stride=2 [route] layers = -1, 8 [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=255 activation=linear [yolo] mask = 1,2,3 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=80 num=6 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1 ================================================ FILE: yolov3.cfg ================================================ [net] # Testing batch=1 subdivisions=1 # Training # batch=64 # subdivisions=16 width=416 height=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 learning_rate=0.001 burn_in=1000 max_batches = 500200 policy=steps steps=400000,450000 scales=.1,.1 [convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky # Downsample [convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=32 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear # Downsample [convolutional] batch_normalize=1 filters=1024 size=3 stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky [shortcut] from=-3 activation=linear ###################### [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky [convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=255 activation=linear [yolo] mask = 6,7,8 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=80 num=9 jitter=.3 ignore_thresh = .5 truth_thresh = 1 random=1 [route] layers = -4 [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [upsample] stride=2 [route] layers = -1, 61 [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=255 activation=linear [yolo] mask = 3,4,5 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=80 num=9 jitter=.3 ignore_thresh = .5 truth_thresh = 1 random=1 [route] layers = -4 [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [upsample] stride=2 [route] layers = -1, 36 [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=255 activation=linear [yolo] mask = 0,1,2 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=80 num=9 jitter=.3 ignore_thresh = .5 truth_thresh = 1 random=1