Super fast and lightweight anchor-free object detection model. Only 1.8mb and run 97FPS on cellphone

NanoDet

Super fast and lightweight anchor-free object detection model. Real-time on mobile devices.

• ⚡Super lightweight: Model file is only 1.8 mb.
• ⚡Super fast: 97fps(10.23ms) on mobile ARM CPU.
• 😎Training friendly: Much lower GPU memory cost than other models. Batch-size=80 is available on GTX1060 6G.
• 😎Easy to deploy: Provide C++ implementation and Android demo based on ncnn inference framework.

Benchmarks

Note:

• Performance is measured on Kirin 980(4xA76+4xA55) ARM CPU based on ncnn. You can test latency on your phone with ncnn_android_benchmark.

• NanoDet mAP(0.5:0.95) is validated on COCO val2017 dataset with no testing time augmentation.

• YOLO mAP refers from Scaled-YOLOv4: Scaling Cross Stage Partial Network

NanoDet is a FCOS-style one-stage anchor-free object detection model which using ATSS for target sampling and using Generalized Focal Loss for classification and box regression. Please refer to these papers for more detail.

Fcos: Fully convolutional one-stage object detection

ATSS:Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

知乎中文介绍 | QQ交流群：908606542 (答案：炼丹)

Demo

Android demo

Android demo project is in demo_android_ncnn folder. Please refer to Android demo guide.

C++ demo

C++ demo based on ncnn is in demo_ncnn folder. Please refer to Cpp demo guide.

Python demo

First, install requirements and setup NanoDet following installation guide. Then download COCO pretrain weight from here👉COCO pretrain weight for torch>=1.6(Google Drive) | COCO pretrain weight for torch<=1.5(Google Drive).

• Inference images

python demo/demo.py image --config CONFIG_PATH --model MODEL_PATH --path IMAGE_PATH

• Inference video

python demo/demo.py video --config CONFIG_PATH --model MODEL_PATH --path VIDEO_PATH

• Inference webcam

python demo/demo.py webcam --config CONFIG_PATH --model MODEL_PATH --camid YOUR_CAMERA_ID

Install

Requirements

• Linux or MacOS
• CUDA >= 10.0
• Python >= 3.6
• Pytorch >= 1.3
• experimental support Windows (Notice: Windows not support distributed training before pytorch1.7)

Step

1. Create a conda virtual environment and then activate it.

 conda create -n nanodet python=3.8 -y
 conda activate nanodet

2. Install pytorch

conda install pytorch torchvision cudatoolkit=11.0 -c pytorch

3. Install requirements

pip install Cython termcolor numpy tensorboard pycocotools matplotlib pyaml opencv-python tqdm

4. Setup NanoDet

git clone https://github.com/RangiLyu/nanodet.git
cd nanodet
python setup.py develop

How to Train

1. Prepare dataset

   Convert your dataset annotations to MS COCO format(COCO annotation format details).

2. Prepare config file

   Copy and modify an example yml config file in config/ folder.

   Change save_path to where you want to save model.

   Change num_classes in model->arch->head.

   Change image path and annotation path in both data->train data->val.

   Set gpu, workers and batch size in device to fit your device.

   Set total_epochs, lr and lr_schedule according to your dataset and batchsize.

   If you want to modify network, data augmentation or other things, please refer to Config File Detail

3. Start training

   For single GPU, run

   python tools/train.py CONFIG_PATH
   

   For multi-GPU, NanoDet using distributed training. (Notice: Windows not support distributed training before pytorch1.7) Please run

   python -m torch.distributed.launch --nproc_per_node=GPU_NUM --master_port 29501 tools/train.py CONFIG_PATH
   

How to Deploy

NanoDet provide C++ and Android demo based on ncnn library.

1. Convert model

   To convert NanoDet pytorch model to ncnn, you can choose this way: pytorch->onnx->ncnn

   To export onnx model, run tools/export.py. Then using onnx-simplifier to simplify onnx structure.

   Run onnx2ncnn in ncnn tools to generate ncnn .param and .bin file.

   After that, using ncnnoptimize to optimize ncnn model.

   If you have quentions about converting ncnn model, refer to ncnn wiki. https://github.com/Tencent/ncnn/wiki

2. Run NanoDet model with C++

   Please refer to demo_ncnn.

3. Run NanoDet on Android

   Please refer to android_demo.

Thanks

https://github.com/Tencent/ncnn

https://github.com/open-mmlab/mmdetection

https://github.com/implus/GFocal

https://github.com/cmdbug/YOLOv5_NCNN

https://github.com/rbgirshick/yacs