- Introduction
- Getting Started
- All pretrained model weights
- Inference
- Test
- Results
- Results
- Contributing
- Credit
This repository contains an op-for-op PyTorch reimplementation of YOLOv3: An Incremental Improvement.
- Python 3.10+
- PyTorch 2.0.0+
- CUDA 11.8+
- Ubuntu 22.04+
pip install yolov3_pytorch -i https://pypi.org/simple
git clone https://github.com/Lornatang/YOLOv3-PyTorch.git
cd YOLOv3-PyTorch
pip install -r requirements.txt
pip install -e .
# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-COCO-20231107.pth.tar -O ./results/pretrained_models/YOLOv3_Tiny-COCO-20231107.pth.tar
python ./tools/inference.py ./data/examples/dog.jpg
# Loaded `./results/pretrained_models/YOLOv3_Tiny-COCO-20231107.pth.tar` models weights successfully.
# image 1/1 ./data/examples/dog.jpg: 320x416 1 bicycle, 2 car, 1 dog,
# See ./results/inference/dog.jpg for visualization.
# Download dataset to `./data`
cd ./scripts
bash ./process_voc0712_dataset.sh
cd ..
# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-VOC-20231107.pth.tar -O ./results/pretrained_models/YOLOv3_Tiny-VOC-20231107.pth.tar
python ./tools/eval.py ./configs/VOC-Detection/yolov3_tiny.yaml
Name | Size | mAPval 0.5:0.95 |
FLOPs(G) | Parameters(M) | Memory(MB) | download |
---|---|---|---|---|---|---|
yolov3_tiny | 416 | 18.7 | 5.6 | 0.71 | 8.9 | model |
yolov3_tiny_prn | 416 | 11.1 | 3.5 | 0.66 | 4.9 | model |
yolov3 | 416 | 66.7 | 66.2 | 0.88 | 61.9 | model |
yolov3_spp | 416 | 66.7 | 66.5 | 0.88 | 63.0 | model |
Model | Size | mAPval 0.5:0.95 |
FLOPs(B) | Memory(MB) | Parameters(M) | download |
---|---|---|---|---|---|---|
yolov3_tiny | 416 | 58.8 | 5.5 | 0.27 | 8.7 | model |
yolov3_tiny_prn | 416 | 47.9 | 3.5 | 0.27 | 4.9 | model |
yolov3 | 416 | 82.9 | 65.7 | 0.61 | 61.6 | model |
yolov3_spp | 416 | 83.2 | 66.1 | 0.88 | 62.7 | model |
yolov3_mobilenetv1 | 416 | 65.6 | 6.6 | 0.69 | 6.2 | model |
yolov3_mobilenetv2 | 416 | 68.2 | 3.5 | 0.49 | 4.3 | model |
yolov3_vgg16 | 416 | 74.1 | 122.8 | 0.74 | 35.5 | model |
# Download dataset to `./data`
cd ./scripts
bash ./process_voc0712_dataset.sh
cd ..
# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-VOC-20231107.pth.tar -O ./results/pretrained_models/YOLOv3_Tiny-VOC-20231107.pth.tar
# change WEIGHTS_PATH in ./configs/VOC-Detection/yolov3_tiny.yaml
python ./tools/train.py ./configs/VOC-Detection/yolov3_tiny.yaml
# COCO2014
# Download dataset to `./data`
cd ./scripts
bash ./process_coco2014_dataset.sh
cd ..
# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-COCO-20231107.pth.tar -O ./results/pretrained_models/YOLOv3_Tiny-COCO-20231107.pth.tar
# change WEIGHTS_PATH in ./configs/COCO-Detection/yolov3_tiny.yaml
python ./tools/train.py ./configs/COCO-Detection/yolov3_tiny.yaml
Details see CustomDataset.md.
If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
I look forward to seeing what the community does with these models!
Joseph Redmon, Ali Farhadi
Abstract
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained
this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though,
don’t worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at
the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5
AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online
at https://pjreddie.com/yolo/.
[Paper] [Project Webpage] [Authors' Implementation]
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}