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Pytorch implements yolov3.Good performance, easy to use, fast speed.
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Lornatang/YOLOv3-PyTorch
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- Introduction
- Getting Started
- All pretrained model weights
- Inference
- Test
- Results
- Results
- Contributing
- Credit
This repository contains an op-for-op PyTorch reimplementation ofYOLOv3: 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.gitcd YOLOv3-PyTorchpip install -r requirements.txtpip 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.tarpython ./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 ./scriptsbash ./process_voc0712_dataset.shcd ..# 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.tarpython ./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 ./scriptsbash ./process_voc0712_dataset.shcd ..# 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.yamlpython ./tools/train.py ./configs/VOC-Detection/yolov3_tiny.yaml
# COCO2014# Download dataset to `./data`cd ./scriptsbash ./process_coco2014_dataset.shcd ..# 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.yamlpython ./tools/train.py ./configs/COCO-Detection/yolov3_tiny.yaml
Details seeCustomDataset.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 trainedthis 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 atthe old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is onlineathttps://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}}
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Pytorch implements yolov3.Good performance, easy to use, fast speed.
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