NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
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Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices.

CI testing Codecov GitHub license Github downloads GitHub release (latest by date)

  • Super lightweight: Model file is only 980KB(INT8) or 1.8MB(FP16).
  • Super fast: 97fps(10.23ms) on mobile ARM CPU.
  • High accuracy: Up to 34.3 mAPval@0.5:0.95 and still realtime on CPU.
  • Training friendly: Much lower GPU memory cost than other models. Batch-size=80 is available on GTX1060 6G.
  • Easy to deploy: Support various backends including ncnn, MNN and OpenVINO. Also provide Android demo based on ncnn inference framework.


NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.

In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. These improvements boost previous NanoDet's detection accuracy by 7 mAP on COCO dataset.



QQ908606542 ()


Model Resolution mAPval
CPU Latency
ARM Latency
FLOPS Params Model Size
NanoDet-m 320*320 20.6 4.98ms 10.23ms 0.72G 0.95M 1.8MB(FP16) | 980KB(INT8)
NanoDet-Plus-m 320*320 27.0 5.25ms 11.97ms 0.9G 1.17M 2.3MB(FP16) | 1.2MB(INT8)
NanoDet-Plus-m 416*416 30.4 8.32ms 19.77ms 1.52G 1.17M 2.3MB(FP16) | 1.2MB(INT8)
NanoDet-Plus-m-1.5x 320*320 29.9 7.21ms 15.90ms 1.75G 2.44M 4.7MB(FP16) | 2.3MB(INT8)
NanoDet-Plus-m-1.5x 416*416 34.1 11.50ms 25.49ms 2.97G 2.44M 4.7MB(FP16) | 2.3MB(INT8)
YOLOv3-Tiny 416*416 16.6 - 37.6ms 5.62G 8.86M 33.7MB
YOLOv4-Tiny 416*416 21.7 - 32.81ms 6.96G 6.06M 23.0MB
YOLOX-Nano 416*416 25.8 - 23.08ms 1.08G 0.91M 1.8MB(FP16)
YOLOv5-n 640*640 28.4 - 44.39ms 4.5G 1.9M 3.8MB(FP16)
FBNetV5 320*640 30.4 - - 1.8G - -
MobileDet 320*320 25.6 - - 0.9G - -

Download pre-trained models and find more models in Model Zoo or in Release Files

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

  • Intel CPU Performance is measured Intel Core-i7-8700 based on OpenVINO.

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

  • YOLOv3&YOLOv4 mAP refers from Scaled-YOLOv4: Scaling Cross Stage Partial Network.


  • [2023.01.20] Upgrade to pytorch-lightning-1.9. The minimum PyTorch version is upgraded to 1.10. Support FP16 training(Thanks @crisp-snakey). Support ignore label(Thanks @zero0kiriyu).

  • [2022.08.26] Upgrade to pytorch-lightning-1.7. The minimum PyTorch version is upgraded to 1.9. To use previous version of PyTorch, please install NanoDet <= v1.0.0-alpha-1

  • [2021.12.25] NanoDet-Plus release! Adding AGM(Assign Guidance Module) & DSLA(Dynamic Soft Label Assigner) to improve 7 mAP with only a little cost.

Find more update notes in Update notes.


Android demo


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

Here is a better implementation ncnn-android-nanodet

NCNN C++ demo

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

MNN demo

Inference using Alibaba's MNN framework is in demo_mnn folder. Please refer to MNN demo guide.

OpenVINO demo

Inference using OpenVINO is in demo_openvino folder. Please refer to OpenVINO demo guide.

Web browser demo

Pytorch demo

First, install requirements and setup NanoDet following installation guide. Then download COCO pretrain weight from here

COCO pretrain checkpoint

The pre-trained weight was trained by the config config/nanodet-plus-m_416.yml.

  • Inference images
python demo/ image --config CONFIG_PATH --model MODEL_PATH --path IMAGE_PATH
  • Inference video
python demo/ video --config CONFIG_PATH --model MODEL_PATH --path VIDEO_PATH
  • Inference webcam
python demo/ webcam --config CONFIG_PATH --model MODEL_PATH --camid YOUR_CAMERA_ID

Besides, We provide a notebook here to demonstrate how to make it work with PyTorch.



  • Linux or MacOS
  • CUDA >= 10.2
  • Python >= 3.7
  • Pytorch >= 1.10.0, <2.0.0


  1. Create a conda virtual environment and then activate it.
 conda create -n nanodet python=3.8 -y
 conda activate nanodet
  1. Install pytorch
conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c conda-forge
  1. Clone this repository
git clone
cd nanodet
  1. Install requirements
pip install -r requirements.txt
  1. Setup NanoDet
python develop

Model Zoo

NanoDet supports variety of backbones. Go to the config folder to see the sample training config files.

Model Backbone Resolution COCO mAP FLOPS Params Pre-train weight
NanoDet-m ShuffleNetV2 1.0x 320*320 20.6 0.72G 0.95M Download
NanoDet-Plus-m-320 (NEW) ShuffleNetV2 1.0x 320*320 27.0 0.9G 1.17M Weight | Checkpoint
NanoDet-Plus-m-416 (NEW) ShuffleNetV2 1.0x 416*416 30.4 1.52G 1.17M Weight | Checkpoint
NanoDet-Plus-m-1.5x-320 (NEW) ShuffleNetV2 1.5x 320*320 29.9 1.75G 2.44M Weight | Checkpoint
NanoDet-Plus-m-1.5x-416 (NEW) ShuffleNetV2 1.5x 416*416 34.1 2.97G 2.44M Weight | Checkpoint

Notice: The difference between Weight and Checkpoint is the weight only provide params in inference time, but the checkpoint contains training time params.

Legacy Model Zoo

Model Backbone Resolution COCO mAP FLOPS Params Pre-train weight
NanoDet-m-416 ShuffleNetV2 1.0x 416*416 23.5 1.2G 0.95M Download
NanoDet-m-1.5x ShuffleNetV2 1.5x 320*320 23.5 1.44G 2.08M Download
NanoDet-m-1.5x-416 ShuffleNetV2 1.5x 416*416 26.8 2.42G 2.08M Download
NanoDet-m-0.5x ShuffleNetV2 0.5x 320*320 13.5 0.3G 0.28M Download
NanoDet-t ShuffleNetV2 1.0x 320*320 21.7 0.96G 1.36M Download
NanoDet-g Custom CSP Net 416*416 22.9 4.2G 3.81M Download
NanoDet-EfficientLite EfficientNet-Lite0 320*320 24.7 1.72G 3.11M Download
NanoDet-EfficientLite EfficientNet-Lite1 416*416 30.3 4.06G 4.01M Download
NanoDet-EfficientLite EfficientNet-Lite2 512*512 32.6 7.12G 4.71M Download
NanoDet-RepVGG RepVGG-A0 416*416 27.8 11.3G 6.75M Download

How to Train

  1. Prepare dataset

    If your dataset annotations are pascal voc xml format, refer to config/nanodet_custom_xml_dataset.yml

    Otherwise, if your dataset annotations are YOLO format (Darknet TXT), refer to config/nanodet-plus-m_416-yolo.yml

    Or 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_dir to where you want to save model.

    Change num_classes in model->arch->head.

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

    Set gpu ids, num 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

    NanoDet is now using pytorch lightning for training.

    For both single-GPU or multiple-GPUs, run:

    python tools/ CONFIG_FILE_PATH
  4. Visualize Logs

    TensorBoard logs are saved in save_dir which you set in config file.

    To visualize tensorboard logs, run:

    cd <YOUR_SAVE_DIR>
    tensorboard --logdir ./

How to Deploy

NanoDet provide multi-backend C++ demo including ncnn, OpenVINO and MNN. There is also an Android demo based on ncnn library.

Export model to ONNX

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

To export onnx model, run tools/

python tools/ --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

Run NanoDet in C++ with inference libraries


Please refer to demo_ncnn.


Please refer to demo_openvino.


Please refer to demo_mnn.

Run NanoDet on Android

Please refer to android_demo.


If you find this project useful in your research, please consider cite:

    title={NanoDet-Plus: Super fast and high accuracy lightweight anchor-free object detection model.},
    howpublished = {\url{}},







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