Package: yolov3-pytorch
YOLOv3-PyTorchContentsIntroductionGetting StartedRequirementsFrom PyPILocal InstallInference (TODO)All pretrained model weightsHow Test and TrainTest yolov3_tiny_voc modelTrain yolov3_tiny_voc modelResume train yolov3_tiny_voc modelResultContributingCreditYOLOv3: An Incremental ImprovementIntroductionThis repository contains an op-for-op PyTorch reimplementation ofYOLOv3: An Incremental Improvement.Getting StartedRequirementsPython 3.10+PyTorch 2.0.0+CUDA 11.8+Ubuntu 22.04+From PyPIpip3installyolov3_pytorch-ihttps://pypi.org/simpleLocal Installgitclonehttps://github.com/Lornatang/YOLOv3-PyTorch.gitcdYOLOv3-PyTorch
pip3install-rrequirements.txt
pip3install-e.All pretrained model weightsGoogle DriverBaidu DriverInference (e.g YOLOv3_Tiny-VOC0712)# Download YOLOv3_Tiny-VOC0712 model weights to `./results/pretrained_models`wgethttps://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-VOC0712-20231107.pth.tar-O./resutls/pretrained_models/YOLOv3_Tiny-VOC0712-20231107.pth.tar
python3./tools/inference.py./data/examples/dog.jpg# Result will be saved to `./results/predict/YOLOv3_Tiny-VOC0712/dog.jpg`TestVOC0712# Download dataset to `./data`cd./scripts
bash./process_voc0712_dataset.shcd..# Download pretrained model weights to `./results/pretrained_models`wgethttps://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-VOC0712-20231107.pth.tar-O./resutls/pretrained_models/YOLOv3_Tiny-VOC0712-20231107.pth.tar
python3./tools/test.py./configs/YOLOv3_Tiny-VOC0712.yamlResultsCOCO2014ModelSizemAPval0.5:0.95FLOPs(G)Parameters(M)Memory(MB)YOLOv3_Tiny-COCO201441618.75.60.718.9YOLOv3_Tiny_PRN-COCO201441611.13.50.664.9YOLOv3-COCO201441666.766.20.8861.9YOLOv3_SPP-COCO201441666.766.50.8863.0VOCModelSizemAPval0.5:0.95FLOPs(B)Memory(MB)Parameters(M)YOLOv3_Tiny-VOC071241658.85.50.278.7YOLOv3_Tiny_PRN-VOC071241647.93.50.274.9YOLOv3-VOC071241682.965.70.6161.6YOLOv3_SPP-VOC071241683.266.10.8862.7YOLOv3_MobileNetV1-VOC071241665.66.60.696.2YOLOv3_MobileNetV2-VOC071241668.23.50.494.3YOLOv3_MobileNetV3_Large-VOC071241670.12.80.504.7YOLOv3_MobileNetV3_Small-VOC071241653.71.50.482.8YOLOv3_VGG16-VOC071241674.1122.80.7435.5TrainVOC0712# Download dataset to `./data`cd./scripts
bash./process_voc0712_dataset.shcd..# Download pretrained model weights to `./results/pretrained_models`wgethttps://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-VOC0712-20231107.pth.tar-O./resutls/pretrained_models/YOLOv3_Tiny-VOC0712-20231107.pth.tar
python3./tools/train.py./configs/YOLOv3_Tiny-VOC0712.yamlCOCO2014 & COCO2017# COCO2014# Download dataset to `./data`cd./scripts
bash./process_coco2014_dataset.shcd..# Download pretrained model weights to `./results/pretrained_models`wgethttps://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-COCO2014-20231107.pth.tar-O./resutls/pretrained_models/YOLOv3_Tiny-COCO2014-20231107.pth.tar
python3./tools/train.py./configs/YOLOv3_Tiny-COCO2014.yaml# COCO2017# Download dataset to `./data`cd./scripts
bash./process_coco2017_dataset.shcd..# Download pretrained model weights to `./results/pretrained_models`wgethttps://github.com/Lornatang/YOLOv3-PyTorch/releases/download/0.1.5/YOLOv3_Tiny-COCO2017-20231107.pth.tar-O./resutls/pretrained_models/YOLOv3_Tiny-COCO2017-20231107.pth.tar
python3./tools/train.py./configs/YOLOv3_Tiny-COCO2017.yamlCustom datasetDetails seeCustomDataset.md.ContributingIf 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!CreditYOLOv3: An Incremental ImprovementJoseph Redmon, Ali FarhadiAbstractWe 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
athttps://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}}