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Welcome to AIRDet! AIRDet is an efficiency-oriented anchor-free object detector, aims to enable robust object detection in various industry scene. With simple design, AIRDet-s outperforms series competitor e.g.(YOLOX-s, MT-YOLOv6-s, PP-YOLOE-s), and still maintains fast speed. Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment.
- [2022/06/23: We release AIRDet-0.0.1!]
- Release AIRDet-series object detection models, e.g. AIRDet-s and AIRDet-m. AIRDet-s achievs mAP as 44.2% on COCO val dataset and 2.8ms latency on Nvidia-V100. AIRDet-m is a larger model build upon AIRDet-s in a heavy neck paradigm, which achieves robust improvement in detection of different object scales. For more information, please refer to Giraffe-neck.
- Release model convert tools for esay deployment, surppots onnx and tensorRT-fp32, TensorRT-fp16.
- High efficient backbone.
- AIRDet-tiny and AIRDet-nano.
- Model distillation.
| Model | size | mAPval 0.5:0.95 |
Latency V100 TRT-FP32-BS32 |
Latency V100 TRT-FP16-BS32 |
FLOPs (G) |
weights |
|---|---|---|---|---|---|---|
| Yolox-s | 640 | 40.5 | 3.4 | 2.3 | 26.81 | link |
| AIRDet-s | 640 | 44.2 | 4.4 | 2.8 | 27.56 | link |
| AIRDet-m | 640 | 48.2 | 8.3 | 4.4 | 76.61 | link |
- We report the mAP of models on COCO2017 validation set.
- The latency in this table are measured without post-processing.
Installation
Step1. Install AIRDet.
git clone https://github.com/tinyvision/AIRDet.git
cd AIRDet/
conda create -n AIRDet python=3.7 -y
conda activate AIRDet
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
export PYTHONPATH=$PWD:$PYTHONPATHStep2. Install pycocotools.
pip3 install cython;
pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'Demo
Step1. Download a pretrained model from the benchmark table, e.g. airdet-s.
Step2. Use -f(config filename) to specify your detector's config. For example:
python tools/demo.py -f configs/airdet_s.py --ckpt /path/to/your/airdet_s.pth --path assets/dog.jpgReproduce our results on COCO
Step1. Prepare COCO dataset
cd <AIRDet Home>
ln -s /path/to/your/coco ./datasets/cocoStep 2. Reproduce our results on COCO by specifying -f(config filename)
python -m torch.distributed.launch --nproc_per_node=8 tools/train.py -f configs/airdet_s.pyEvaluation
python -m torch.distributed.launch --nproc_per_node=8 tools/eval.py -f configs/airdet_s.py --ckpt /path/to/your/airdet_s_ckpt.pthTraining on Custom Data
Airdet supports COCO and VOC format. Before training, you need to transform your data into COCO or VOC format. We provide the usage of COCO format in default config. If you are trying to use VOC format, here is a breif example.Step.1 Transform your own dataset into VOC format. the directory structure should be as follow:
Bus/
Annotations/
*.xml
JPEGImages/
*.jpg,png,PNG
ImageSets/
Main/
train.txt
test.txt
val.txtStep.2 Write the corresponding dataset name and Train/Eval dataset path, the dataset name should be like [xxx_custom_train/val].
self.dataset.train_ann = ("bus_custom_train",)
self.dataset.val_ann = ("bus_custom_val")
self.dataset.data_dir = 'datasets'
self.dataset.data_list = {
"bus_custom_train": {
"data_dir": "Bus/",
"split": "train"
},
"bus_custom_val": {
"data_dir": "Bus/",
"split": "val"
},
}
self.dataset.class2id = {
'class_name1': 1,
'class_name2': 2,
'class_name3': 3,
}
self.model.head.num_classes = len(self.dataset.class2id.keys())Step.3 Put your dataset under $AIRDet/datasets.
ln -s /path/to/your/Bus/ ./datasets/Bus/Step.4 Create your config file to control everything, including model setting, training setting, and test setting, e.g. bus_s.py.
python -m torch.distributed.launch --nproc_per_node=8 tools/train.py -f configs/bus_s.pyInstallation
Step1. Install ONNX.
pip install onnx==1.8.1
pip install onnxruntime==1.8.0
pip install onnx-simplifier==0.3.5Step2. Install CUDA、CuDNN、TensorRT and pyCUDA 2.1 CUDA
wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
sudo sh cuda_10.2.89_440.33.01_linux.run
export PATH=$PATH:/usr/local/cuda-10.2/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib64
source ~/.bashrc2.2 CuDNN
sudo cp cuda/include/* /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*2.3 TensorRT
cd TensorRT-7.2.1.6/python
pip install tensorrt-7.2.1.6-cp37-none-linux_x86_64.whl
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:TensorRT-7.2.1.6/lib2.4 pycuda
pip install pycuda==2022.1Step.1 convert torch model to onnx or trt engine, and the output file would be generated in deploy/. Note the convert mode has three options:[onnx, trt_32, trt_16].
python tools/converter.py --output-name deploy/airdet_s.onnx -f configs/airdet_s.py -c airdet_s.pth --batch_size 1 --img_size 640 --mode trt_32Step.2 trt engine evaluation and inference speed computation and appoint trt engine by --trt.
python -m torch.distributed.launch --nproc_per_node=1 tools/trt_eval.py -f configs/airdet_s.py --trt deploy/airdet_s_32.trt --batch_size 1 --img_size 640Step.3 trt engine inference demo and appoint test image by -p.
python tools/trt_inference.py -f configs/airdet_s.py -t deploy/airdet_s_32.trt -p assets/dog.jpg --img_size 640 --nms 0.7