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Repository: tjmoon0104/pytorch-tiny-imagenet
Branch: master
Commit: 88e2702b2b0f
Files: 13
Total size: 895.1 KB

Directory structure:
gitextract_lrtpih07/

├── .gitignore
├── Makefile
├── README.md
├── notebook/
│   ├── AlexNet.ipynb
│   ├── ResNet18_224.ipynb
│   ├── ResNet18_64.ipynb
│   └── ResNet18_FineTune.ipynb
├── pyproject.toml
├── requirements.txt
└── src/
    ├── model/
    │   └── AlexNet.py
    ├── test_model.py
    ├── train_model.py
    └── util/
        └── prepare_dataset.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
models
tiny-224
tiny-imagenet-200
tiny-imagenet-200.zip
*.zip
__pycache__
.*
!/.gitignore

================================================
FILE: Makefile
================================================
.PHONY: export-req

export-req:
	uv export --no-hashes --format requirements-txt > requirements.txt


================================================
FILE: README.md
================================================
# Pytorch-Tiny-ImageNet

### Installation

If you are familiar with [uv](https://docs.astral.sh/uv/getting-started/installation/), you can install dependencies with `uv sync`.
Otherwise, you can install dependencies with requirements.txt

**Trouble shooting** with OpenCV [here](https://github.com/NVIDIA/nvidia-docker/issues/864#issuecomment-452023152)

### Dataset

`python src/util/prepare_dataset.py` will download and preprocess tiny-imagenet dataset.
In the original dataset, there are 200 classes, and each class has 500 images.
However, in test dataset there are no labels, so I split the validation dataset into validation and test dataset. (25 per class)
Probably not the best train(500), val(25), test(25) splitting method, but I think it's good enough for this project to evaluate transfer learning.

The dataset is then resized from 64x64 to 224x224.

If you don't want to prepare dataset, you can download dataset
- [tiny-imagenet-200](https://github.com/tjmoon0104/pytorch-tiny-imagenet/releases/download/tiny-imagenet-dataset/processed-tiny-imagenet-200.zip)
- [tiny-imagenet-200 resized to 224x224](https://github.com/tjmoon0104/pytorch-tiny-imagenet/releases/download/tiny-imagenet-dataset/tiny-224.zip)

### Summary

Goal of this project is to evaluate transfer learning on tiny-imagenet dataset.

Tiny-ImageNet dataset has images of size 64x64, but ImageNet dataset is trained on 224x224 images.
To match the input size, I resized tiny-imagenet dataset to 224x224 and trained on pretrained weight from ImageNet.

Finetune few layers, and use pretrained weight from 224x224 trained model to retrain 64x64 image on ResNet18

### Test Result

| Model    | Test Result | Input size | pretrained weight |
| -------- | ----------- | ---------- | ----------------- |
| AlexNet  | 35.88%      | 64x64      | ImageNet          |
| ResNet18 | 53.58%      | 64x64      | ImageNet          |
| ResNet18 | 69.62%      | 224x224    | ImageNet          |
| ResNet18 | 69.80%      | 64x64      | Model Above       |

### Updates

This project was done as part of Udacity Machine Learning Engineer Nanodegree Capstone Project in 2018.

I haven't updated the code since then, but I decided to update this project thanks to a lot of interest and stars from the community.

Since then, I have been working as Data Engineer, and I have improved my python programming skills.

This version includes the following changes:
- uv for dependency management
- Pytorch with M1 Mac GPU support
- Dataset download and preprocessing with python


================================================
FILE: notebook/AlexNet.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T19:31:05.125375Z",
     "start_time": "2025-09-16T19:31:03.945301Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "from src.model.AlexNet import alexnet\n",
    "from src.test_model import test_model\n",
    "from src.train_model import train_model\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T19:31:05.130298Z",
     "start_time": "2025-09-16T19:31:05.128750Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = \"cuda:0\"\n",
    "elif torch.backends.mps.is_built():\n",
    "    device = torch.device(\"mps\")\n",
    "else:\n",
    "    device = \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T19:31:05.494216Z",
     "start_time": "2025-09-16T19:31:05.132664Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    \"train\": transforms.Compose([transforms.ToTensor()]),\n",
    "    \"val\": transforms.Compose([transforms.ToTensor()]),\n",
    "    \"test\": transforms.Compose([transforms.ToTensor()]),\n",
    "}\n",
    "\n",
    "data_dir = Path(\"../tiny-imagenet-200/\")\n",
    "num_workers = {\"train\": 2, \"val\": 0, \"test\": 0}\n",
    "image_datasets = {\n",
    "    x: datasets.ImageFolder(data_dir / x, data_transforms[x]) for x in [\"train\", \"val\", \"test\"]\n",
    "}\n",
    "dataloaders = {\n",
    "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=500, shuffle=True, num_workers=num_workers[x])\n",
    "    for x in [\"train\", \"val\", \"test\"]\n",
    "}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T19:31:05.734421Z",
     "start_time": "2025-09-16T19:31:05.497501Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load AlexNet\n",
    "torch.manual_seed(42)\n",
    "model_ft = alexnet().to(device)\n",
    "\n",
    "# Loss Function\n",
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.005, max:    0.005, cur:    0.005)\n",
      "\tvalidation       \t (min:    0.005, max:    0.005, cur:    0.005)\n",
      "log loss\n",
      "\ttraining         \t (min:    5.298, max:    5.298, cur:    5.298)\n",
      "\tvalidation       \t (min:    5.298, max:    5.298, cur:    5.298)\n",
      "Training complete in 1m 60s\n",
      "Best Validation Accuracy: 0.004999999888241291, Epoch: 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "train_model(\n",
    "    output_path=\"Alex_not_pretrained\",\n",
    "    model=model_ft,\n",
    "    dataloaders=dataloaders,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer_ft,\n",
    "    device=device,\n",
    "    num_epochs=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.005, max:    0.238, cur:    0.238)\n",
      "\tvalidation       \t (min:    0.006, max:    0.292, cur:    0.292)\n",
      "log loss\n",
      "\ttraining         \t (min:    3.322, max:    5.298, cur:    3.322)\n",
      "\tvalidation       \t (min:    3.084, max:    5.297, cur:    3.084)\n",
      "Training complete in 7m 59s\n",
      "Best Validation Accuracy: 0.29179999232292175, Epoch: 20\n"
     ]
    }
   ],
   "source": [
    "# Load AlexNet with pretrained weights\n",
    "model_ft = alexnet(pretrained=True).to(device)\n",
    "\n",
    "# Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Train\n",
    "best_epoch = train_model(\n",
    "    output_path=\"Alex_pretrained\",\n",
    "    model=model_ft,\n",
    "    dataloaders=dataloaders,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer_ft,\n",
    "    device=device,\n",
    "    num_epochs=20,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 3.1334 Acc: 0.2780\n",
      "Test complete in 0m 1s\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "model_ft.load_state_dict(torch.load(f\"models/Alex_pretrained/model_{best_epoch}_epoch.pt\"))\n",
    "test_model(model=model_ft, dataloaders=dataloaders, criterion=criterion, device=device)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: notebook/ResNet18_224.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-02T09:10:38.914114Z",
     "start_time": "2023-12-02T09:10:37.879698Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "from src.test_model import test_model\n",
    "from src.train_model import train_model\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = \"cuda:0\"\n",
    "elif torch.backends.mps.is_built():\n",
    "    device = torch.device(\"mps\")\n",
    "else:\n",
    "    device = \"cpu\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-02T09:10:38.916852Z",
     "start_time": "2023-12-02T09:10:38.915071Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-02T09:10:39.069142Z",
     "start_time": "2023-12-02T09:10:38.918664Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = \"../tiny-224/\"\n",
    "num_workers = {\"train\": 4, \"val\": 0, \"test\": 0}\n",
    "data_transforms = {\n",
    "    \"train\": transforms.Compose(\n",
    "        [\n",
    "            transforms.RandomRotation(20),\n",
    "            transforms.RandomHorizontalFlip(0.5),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "        ]\n",
    "    ),\n",
    "    \"val\": transforms.Compose(\n",
    "        [\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "        ]\n",
    "    ),\n",
    "    \"test\": transforms.Compose(\n",
    "        [\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n",
    "        ]\n",
    "    ),\n",
    "}\n",
    "image_datasets = {\n",
    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in [\"train\", \"val\", \"test\"]\n",
    "}\n",
    "dataloaders = {\n",
    "    x: data.DataLoader(image_datasets[x], batch_size=100, shuffle=True, num_workers=num_workers[x])\n",
    "    for x in [\"train\", \"val\", \"test\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-02T09:10:39.291426Z",
     "start_time": "2023-12-02T09:10:39.069883Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load Resnet18\n",
    "torch.manual_seed(42)\n",
    "model_ft = models.resnet18(weights=\"IMAGENET1K_V1\")\n",
    "# Finetune Final few layers to adjust for tiny imagenet input\n",
    "model_ft.avgpool = nn.AdaptiveAvgPool2d(1)\n",
    "num_features = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_features, 200)\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "# Loss Function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 2 Axes>",
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Download .txt
gitextract_lrtpih07/

├── .gitignore
├── Makefile
├── README.md
├── notebook/
│   ├── AlexNet.ipynb
│   ├── ResNet18_224.ipynb
│   ├── ResNet18_64.ipynb
│   └── ResNet18_FineTune.ipynb
├── pyproject.toml
├── requirements.txt
└── src/
    ├── model/
    │   └── AlexNet.py
    ├── test_model.py
    ├── train_model.py
    └── util/
        └── prepare_dataset.py
Download .txt
SYMBOL INDEX (9 symbols across 4 files)

FILE: src/model/AlexNet.py
  class AlexNet (line 13) | class AlexNet(nn.Module):
    method __init__ (line 14) | def __init__(self, num_classes=1000):
    method forward (line 41) | def forward(self, x):
  function alexnet (line 48) | def alexnet(pretrained=False, **kwargs):

FILE: src/test_model.py
  function test_model (line 6) | def test_model(model, dataloaders, criterion, device):

FILE: src/train_model.py
  function train_model (line 8) | def train_model(output_path, model, dataloaders, criterion, optimizer, d...

FILE: src/util/prepare_dataset.py
  function show_progress (line 26) | def show_progress(block_num, block_size, total_size):
  function split_list_randomly (line 80) | def split_list_randomly(input_list: list[str], split_ratio=0.5) -> dict[...
  function resize_img (line 117) | def resize_img(image_path: Path, size: int = 224) -> None:
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (924K chars).
[
  {
    "path": ".gitignore",
    "chars": 89,
    "preview": "models\ntiny-224\ntiny-imagenet-200\ntiny-imagenet-200.zip\n*.zip\n__pycache__\n.*\n!/.gitignore"
  },
  {
    "path": "Makefile",
    "chars": 100,
    "preview": ".PHONY: export-req\n\nexport-req:\n\tuv export --no-hashes --format requirements-txt > requirements.txt\n"
  },
  {
    "path": "README.md",
    "chars": 2586,
    "preview": "# Pytorch-Tiny-ImageNet\r\n\r\n### Installation\r\n\r\nIf you are familiar with [uv](https://docs.astral.sh/uv/getting-started/i"
  },
  {
    "path": "notebook/AlexNet.ipynb",
    "chars": 188499,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\""
  },
  {
    "path": "notebook/ResNet18_224.ipynb",
    "chars": 81671,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\""
  },
  {
    "path": "notebook/ResNet18_64.ipynb",
    "chars": 537310,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\""
  },
  {
    "path": "notebook/ResNet18_FineTune.ipynb",
    "chars": 86997,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time"
  },
  {
    "path": "pyproject.toml",
    "chars": 511,
    "preview": "[tool.ruff]\nline-length = 119\n\n[project]\nname = \"pytorch-tiny-imagenet\"\nversion = \"0.2.0\"\ndescription = \"\"\nreadme = \"REA"
  },
  {
    "path": "requirements.txt",
    "chars": 9082,
    "preview": "# This file was autogenerated by uv via the following command:\n#    uv export --no-hashes --format requirements-txt\nanyi"
  },
  {
    "path": "src/model/AlexNet.py",
    "chars": 1916,
    "preview": "import torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\n\n\n__all__ = [\"AlexNet\", \"alexnet\"]\n\n\nmodel_urls = {\n    "
  },
  {
    "path": "src/test_model.py",
    "chars": 1067,
    "preview": "import time\n\nimport torch\n\n\ndef test_model(model, dataloaders, criterion, device):\n    since = time.time()\n    phase = \""
  },
  {
    "path": "src/train_model.py",
    "chars": 2463,
    "preview": "import time\nfrom pathlib import Path\n\nimport torch\nfrom livelossplot import PlotLosses\n\n\ndef train_model(output_path, mo"
  },
  {
    "path": "src/util/prepare_dataset.py",
    "chars": 4307,
    "preview": "import random\nimport shutil\nfrom hashlib import md5\nfrom pathlib import Path\nfrom urllib.request import urlretrieve\nfrom"
  }
]

About this extraction

This page contains the full source code of the tjmoon0104/pytorch-tiny-imagenet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (895.1 KB), approximately 590.7k tokens, and a symbol index with 9 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.

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