Split the yolov5 model to {backbone, neck, head} to facilitate the operation of various modules and support more backbones.Basically, only change the model, and I didn't change the architecture, training and testing of yolov5. Therefore, if the original code is updated, it is also very convenient to update this code. if you have some new ideas, you can give a pull request, add new features together。 if this repo can help you, please give me a star.

• Features
• Notices
• Bugs
• Prerequisites
• Getting Started
  • Dataset Preparation
  • Training and Testing
    • Training
    • Testing and Visualize
  • Model performance comparison
  • Detection
  • Deploy
    • Export
    • Grpc server
• Reference

• Reorganize model structure, such as backbone, neck, head, can modify the network flexibly and conveniently
• mobilenetV3-small, mobilenetV3-large
• shufflenet_v2_x0_5, shufflenet_v2_x1_0, shufflenet_v2_x1_5, shufflenet_v2_x2_0
• yolov5s, yolov5m, yolov5l, yolov5x, yolov5transformer
• resnet18, resnet50, resnet34, resnet101, resnet152
• efficientnet_b0 - efficientnet_b8, efficientnet_l2
• hrnet 18,32,48
• CBAM, SE
• swin transformer - base, tiny, small, large (please set half=False in scripts/eval.py and don't use model.half in train.py)
• DCN (mixed precision training not support, if you want use dcn, please close amp in line 292 of scripts/train.py)
• coord conv
• drop_block
• vgg, repvgg
• tensorrt c++/python infer, triton server infer
• gnn backbone

• The CBAM, SE, DCN, coord conv. At present, the above plug-ins are not added to all networks, so you may need to modify the code yourself.
• The default gw and gd for PAN and FPN of other backbone are same as yolov5_s, so if you want a strong model, please modify self.gw and self.gd in FPN and PAN.
• resnet with dcn, training on gpu *RuntimeError: expected scalar type Half but found Float: please remove the mixed precision training in line 351 of scripts/train.py
• swin-transformer, training is ok, but testing report *RuntimeError: expected object of scalar type Float but got scalar type Half for argument #2 'mat2' in call to_th_bmm_out in swin_trsansformer.py. please set half=False in script/eval.py
• mobilenet export onnx failed, please replace HardSigmoid() by others, because onnx don't support pytorch nn.threshold

None

please refer requirements.txt

Make data for yolov5 format. you can use od/data/transform_voc.py convert VOC data to yolov5 data format.

For training and Testing, it's same like yolov5.

1. check out configs/data.yaml, and replace with your data， and number of object nc
2. check out configs/model_*.yaml, choose backbone. and change nc to your dataset. please refer support_backbone in models.backbone.init.py

$ python scripts/train.py  --batch 16 --epochs 5 --data configs/data.yaml --cfg configs/model_XXX.yaml

A google colab demo in train_demo.ipynb

$ python scripts/eval.py   --data configs/data.yaml  --weights runs/train/yolo/weights/best.py

For some reasons, I can't provide the pretrained weight, only the comparison results. Sorry!

All checkpoints are trained to 300 epochs with default settings. Yolov5 Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml. The mAP of the validation come to the last epoch, maybe not the best.

python scripts/detector.py   --weights yolov5.pth --imgs_root  test_imgs   --save_dir  ./results --img_size  640  --conf_thresh 0.4  --iou_thresh 0.4

python scripts/export.py   --weights yolov5.pth 

python scripts/convert_mobileone-yolov5.py

In projects folder, tf_serving and triton demo are provided.

You can directly quantify the onnx model

This script run succ on Tensorrt 7.x. For 8.x, this code need be rewrite.

python scripts/trt_quant/convert_trt_quant.py  --img_dir  /XXXX/train/  --img_size 640 --batch_size 6 --batch 200 --onnx_model runs/train/exp1/weights/bast.onnx  --mode int8

See

For tensorrt model, you can direct use official trt export, and refer scripts/trt_infer/cpp/. For test, I use TensorRT-8.4.0.6.

privode c++ / python demo, scripts/trt_infer

• ultralytics/yolov5
• EfficientNet-PyTorch
• Mobilenet v3
• resnet
• hrnet
• shufflenet
• swin transformer
• dcn-v2
• coord_conv
• triton server
• drop_block
• trt quan
• repvgg