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.

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Benchmarks

Model	Resolution	COCO mAP	Latency(ARM 4xCore)	FLOPS	Params	Model Size(ncnn bin)
NanoDet-m	320*320	20.6	10.23ms	0.72B	0.95M	1.8mb
NanoDet-m	416*416	21.7	16.44ms	1.2B	0.95M	1.8mb
YoloV3-Tiny	416*416	16.6	37.6ms	5.62B	8.86M	33.7mb
YoloV4-Tiny	416*416	21.7	32.81ms	6.96B	6.06M	23.0mb

Note:

• Performance is measured on Kirin 980(4xA76+4xA55) ARM CPU based on ncnn.

• 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

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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->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

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Install

Requirements

• Linux or MacOS (Windows not support distributed training)
• CUDA >= 10.0
• Python >= 3.6
• Pytorch >= 1.3

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

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How to Train

1. Prepare dataset

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

   If your dataset annotations are pascal voc xml format, using this repo to convert data. 👉 voc2coco

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

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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

2. Run NanoDet model with C++

   Please refer to demo_ncnn.

3. Run NanoDet on Android

   Please refer to android_demo.

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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