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«YOLOv3» reproduced the paper "YOLOv3: An Incremental Improvement"
- Train using the
COCO train2017dataset and test using theCOCO val2017dataset with an input size of416x416. give the result as follows (No version of the COCO dataset used in the paper was found)
| Original (darknet) | DeNA/PyTorch_YOLOv3 | zjykzj/YOLOv3(This) | |
|---|---|---|---|
| ARCH | YOLOv3 | YOLOv3 | YOLOv3 |
| COCO AP[IoU=0.50:0.95] | 0.310 | 0.311 | 0.314(v4.0)/0.315(v2.0) |
| COCO AP[IoU=0.50] | 0.553 | 0.558 | 0.535(v4.0)/0.543(v2.0) |
- Table of Contents
- Latest News
- Background
- Prepare Data
- Installation
- Usage
- Maintainers
- Thanks
- Contributing
- License
- [2023/07/19]v4.0. Add ultralytics/yolov5(485da42) transforms and support AMP training.
- [2023/06/22]v3.2. Remove Excess Code and Implementation.
- [2023/06/22]v3.1. Reconstruct DATA Module and Preprocessing Module.
- [2023/05/24]v3.0. Refer to zjykzj/YOLOv2 to reconstruct the entire project and train
Pascal VOCandCOCOdatasets withYOLOv2Loss. - [2023/04/16]v2.0. Fixed preprocessing implementation, YOLOv3 network performance close to the original paper implementation.
- [2023/02/16]v1.0. implementing preliminary YOLOv3 network training and inference implementation.
The purpose of creating this warehouse is to better understand the YOLO series object detection network. Note: The realization of the project depends heavily on the implementation of DeNA/PyTorch_YOLOv3 and NVIDIA/apex
Use this script voc2yolov5.py
python voc2yolov5.py -s /home/zj/data/voc -d /home/zj/data/voc/voc2yolov5-train -l trainval-2007 trainval-2012
python voc2yolov5.py -s /home/zj/data/voc -d /home/zj/data/voc/voc2yolov5-val -l test-2007Then softlink the folder where the dataset is located to the specified location:
ln -s /path/to/voc /path/to/YOLOv3/../datasets/vocUse this script get_coco.sh
Refer to requirements.txt for installing the training environment
pip install -r requirements.txtDevelopment environment (Use nvidia docker container)
docker run --gpus all -it --rm -v </path/to/YOLOv3>:/app/YOLOv3 -v </path/to/COCO>:/app/YOLOv3/COCO nvcr.io/nvidia/pytorch:22.08-py3- One GPU
CUDA_VISIBLE_DEVICES=0 python main_amp.py -c configs/yolov3_coco.cfg --opt-level=O1 ../datasets/coco- Multi-GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port "36321" main_amp.py -c configs/yolov3_coco.cfg --opt-level=O1 ../datasets/cocopython eval.py -c configs/yolov3_coco.cfg -ckpt outputs/yolov3_coco/model_best.pth.tar ../datasets/coco
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.535
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.323
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.342
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.436
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.594
python eval.py -c configs/yolov3_voc.cfg -ckpt outputs/yolov3_voc/model_best.pth.tar ../datasets/voc
VOC07 metric? Yes
AP for aeroplane = 0.8442
AP for bicycle = 0.8575
AP for bird = 0.7730
AP for boat = 0.6824
AP for bottle = 0.6737
AP for bus = 0.8505
AP for car = 0.8663
AP for cat = 0.8667
AP for chair = 0.6073
AP for cow = 0.8196
AP for diningtable = 0.7213
AP for dog = 0.8433
AP for horse = 0.8761
AP for motorbike = 0.8568
AP for person = 0.8245
AP for pottedplant = 0.5211
AP for sheep = 0.8140
AP for sofa = 0.7385
AP for train = 0.8304
AP for tvmonitor = 0.7727
Mean AP = 0.7820python demo.py -c 0.6 configs/yolov3_coco.cfg outputs/yolov3_coco/model_best.pth.tar --exp coco assets/coco/
python demo.py -c 0.6 configs/yolov3_voc.cfg outputs/yolov3_voc/model_best.pth.tar --exp voc assets/voc2007-test/
- zhujian - Initial work - zjykzj
Anyone's participation is welcome! Open an issue or submit PRs.
Small note:
- Git submission specifications should be complied with Conventional Commits
- If versioned, please conform to the Semantic Versioning 2.0.0 specification
- If editing the README, please conform to the standard-readme specification.
Apache License 2.0 © 2022 zjykzj
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