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https://github.com/rangilyu/nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
https://github.com/rangilyu/nanodet
anchor-free android deep-learning deep-neural-networks efficientnet mnn model-zoo nanodet nanodet-plus ncnn object-detection openvino pytorch repvgg shufflenet
Last synced: 1 day ago
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NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
- Host: GitHub
- URL: https://github.com/rangilyu/nanodet
- Owner: RangiLyu
- License: apache-2.0
- Created: 2020-10-19T08:36:25.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-08-08T02:52:55.000Z (about 2 months ago)
- Last Synced: 2024-09-25T22:20:11.496Z (1 day ago)
- Topics: anchor-free, android, deep-learning, deep-neural-networks, efficientnet, mnn, model-zoo, nanodet, nanodet-plus, ncnn, object-detection, openvino, pytorch, repvgg, shufflenet
- Language: Python
- Homepage:
- Size: 5.17 MB
- Stars: 5,696
- Watchers: 67
- Forks: 1,036
- Open Issues: 238
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NanoDet-Plus
**Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices.**
[](https://img.shields.io/github/checks-status/RangiLyu/nanodet/main?label=CI&style=flat)
[](https://github.com/RangiLyu/nanodet/blob/main/LICENSE)
[](https://img.shields.io/github/downloads/RangiLyu/nanodet/total?color=yellow&label=Downloads&logo=github&logoColor=lightgrey&style=flat)
[](https://img.shields.io/github/v/release/RangiLyu/nanodet?style=flat)
* ⚡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.
****
# Introduction
NanoDet is a FCOS-style one-stage anchor-free object detection model which using [Generalized Focal Loss](https://arxiv.org/pdf/2006.04388.pdf) 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.
[NanoDet-Plus 知乎中文介绍](https://zhuanlan.zhihu.com/p/449912627)
[NanoDet 知乎中文介绍](https://zhuanlan.zhihu.com/p/306530300)
QQ交流群：908606542 (答案：炼丹)
****
## Benchmarks
Model |Resolution| mAPval
0.5:0.95 |CPU Latency
(i7-8700) |ARM Latency
(4xA76) | 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](#model-zoo) or in [Release Files](https://github.com/RangiLyu/nanodet/releases)***
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](https://github.com/nihui/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](https://arxiv.org/abs/2011.08036).
****
## NEWS!!!
* [2023.01.20] Upgrade to [pytorch-lightning-1.9](https://github.com/Lightning-AI/lightning/releases/tag/1.9.0). 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](https://lightning.ai/). The minimum PyTorch version is upgraded to 1.9. To use previous version of PyTorch, please install [NanoDet <= v1.0.0-alpha-1](https://github.com/RangiLyu/nanodet/tags)
* [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](docs/update.md).
## Demo
### Android demo
Android demo project is in ***demo_android_ncnn*** folder. Please refer to [Android demo guide](demo_android_ncnn/README.md).
Here is a better implementation 👉 [ncnn-android-nanodet](https://github.com/nihui/ncnn-android-nanodet)
### NCNN C++ demo
C++ demo based on [ncnn](https://github.com/Tencent/ncnn) is in ***demo_ncnn*** folder. Please refer to [Cpp demo guide](demo_ncnn/README.md).
### MNN demo
Inference using [Alibaba's MNN framework](https://github.com/alibaba/MNN) is in ***demo_mnn*** folder. Please refer to [MNN demo guide](demo_mnn/README.md).
### OpenVINO demo
Inference using [OpenVINO](https://01.org/openvinotoolkit) is in ***demo_openvino*** folder. Please refer to [OpenVINO demo guide](demo_openvino/README.md).
### Web browser demo
https://nihui.github.io/ncnn-webassembly-nanodet/
### Pytorch demo
First, install requirements and setup NanoDet following installation guide. Then download COCO pretrain weight from here
👉[COCO pretrain checkpoint](https://github.com/RangiLyu/nanodet/releases/download/v1.0.0-alpha-1/nanodet-plus-m_416_checkpoint.ckpt)
The pre-trained weight was trained by the config `config/nanodet-plus-m_416.yml`.
* Inference images
```bash
python demo/demo.py image --config CONFIG_PATH --model MODEL_PATH --path IMAGE_PATH
```
* Inference video
```bash
python demo/demo.py video --config CONFIG_PATH --model MODEL_PATH --path VIDEO_PATH
```
* Inference webcam
```bash
python demo/demo.py webcam --config CONFIG_PATH --model MODEL_PATH --camid YOUR_CAMERA_ID
```
Besides, We provide a notebook [here](./demo/demo-inference-with-pytorch.ipynb) to demonstrate how to make it work with PyTorch.
****
## Install
### Requirements
* Linux or MacOS
* CUDA >= 10.2
* Python >= 3.7
* Pytorch >= 1.10.0, <2.0.0
### Step
1. Create a conda virtual environment and then activate it.
```shell script
conda create -n nanodet python=3.8 -y
conda activate nanodet
```
2. Install pytorch
```shell script
conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c conda-forge
```
3. Clone this repository
```shell script
git clone https://github.com/RangiLyu/nanodet.git
cd nanodet
```
4. Install requirements
```shell script
pip install -r requirements.txt
```
5. Setup NanoDet
```shell script
python setup.py develop
```
****
## Model Zoo
NanoDet supports variety of backbones. Go to the [***config*** folder](config/) 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](https://drive.google.com/file/d/1ZkYucuLusJrCb_i63Lid0kYyyLvEiGN3/view?usp=sharing) |
NanoDet-Plus-m-320 (***NEW***) | ShuffleNetV2 1.0x | 320*320 | 27.0 | 0.9G | 1.17M | [Weight](https://drive.google.com/file/d/1Dq0cTIdJDUhQxJe45z6rWncbZmOyh1Tv/view?usp=sharing) | [Checkpoint](https://drive.google.com/file/d/1YvuEhahlgqxIhJu7bsL-fhaqubKcCWQc/view?usp=sharing)
NanoDet-Plus-m-416 (***NEW***) | ShuffleNetV2 1.0x | 416*416 | 30.4 | 1.52G | 1.17M | [Weight](https://drive.google.com/file/d/1FN3WK3FLjBm7oCqiwUcD3m3MjfqxuzXe/view?usp=sharing) | [Checkpoint](https://drive.google.com/file/d/1gFjyrl7O8p5APr1ZOtWEm3tQNN35zi_W/view?usp=sharing)
NanoDet-Plus-m-1.5x-320 (***NEW***)| ShuffleNetV2 1.5x | 320*320 | 29.9 | 1.75G | 2.44M | [Weight](https://drive.google.com/file/d/1Xdlgu5lxiS3w6ER7GE1mZpY663wmpcyY/view?usp=sharing) | [Checkpoint](https://drive.google.com/file/d/1qXR6t3TBMXlz6GlTU3fxiLA-eueYoGrW/view?usp=sharing)
NanoDet-Plus-m-1.5x-416 (***NEW***)| ShuffleNetV2 1.5x | 416*416 | 34.1 | 2.97G | 2.44M | [Weight](https://drive.google.com/file/d/16FJJJgUt5VrSKG7RM_ImdKKzhJ-Mu45I/view?usp=sharing) | [Checkpoint](https://drive.google.com/file/d/17sdAUydlEXCrHMsxlDPLj5cGb-8-mmY6/view?usp=sharing)
*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](https://drive.google.com/file/d/1jY-Um2VDDEhuVhluP9lE70rG83eXQYhV/view?usp=sharing)|
NanoDet-m-1.5x | ShuffleNetV2 1.5x | 320*320 | 23.5 | 1.44G | 2.08M | [Download](https://drive.google.com/file/d/1_n1cAWy622LV8wbUnXImtcvcUVPOhYrW/view?usp=sharing) |
NanoDet-m-1.5x-416 | ShuffleNetV2 1.5x | 416*416 | 26.8 | 2.42G | 2.08M | [Download](https://drive.google.com/file/d/1CCYgwX3LWfN7hukcomhEhGWN-qcC3Tv4/view?usp=sharing)|
NanoDet-m-0.5x | ShuffleNetV2 0.5x | 320*320 | 13.5 | 0.3G | 0.28M | [Download](https://drive.google.com/file/d/1rMHkD30jacjRpslmQja5jls86xd0YssR/view?usp=sharing) |
NanoDet-t | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96G | 1.36M | [Download](https://drive.google.com/file/d/1TqRGZeOKVCb98ehTaE0gJEuND6jxwaqN/view?usp=sharing) |
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2G | 3.81M | [Download](https://drive.google.com/file/d/1f2lH7Ae1AY04g20zTZY7JS_dKKP37hvE/view?usp=sharing)|
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72G | 3.11M | [Download](https://drive.google.com/file/d/1Dj1nBFc78GHDI9Wn8b3X4MTiIV2el8qP/view?usp=sharing)|
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06G | 4.01M | [Download](https://drive.google.com/file/d/1ernkb_XhnKMPdCBBtUEdwxIIBF6UVnXq/view?usp=sharing) |
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12G | 4.71M | [Download](https://drive.google.com/file/d/11V20AxXe6bTHyw3aMkgsZVzLOB31seoc/view?usp=sharing) |
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3G | 6.75M | [Download](https://drive.google.com/file/d/1nWZZ1qXb1HuIXwPSYpEyFHHqX05GaFer/view?usp=sharing) |
****
## How to Train
1. **Prepare dataset**
If your dataset annotations are pascal voc xml format, refer to [config/nanodet_custom_xml_dataset.yml](config/nanodet_custom_xml_dataset.yml)
Otherwise, if your dataset annotations are YOLO format ([Darknet TXT](https://github.com/AlexeyAB/Yolo_mark/issues/60#issuecomment-401854885)), refer to [config/nanodet-plus-m_416-yolo.yml](config/nanodet-plus-m_416-yolo.yml)
Or convert your dataset annotations to MS COCO format[(COCO annotation format details)](https://cocodataset.org/#format-data).
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](docs/config_file_detail.md)
3. **Start training**
NanoDet is now using [pytorch lightning](https://github.com/PyTorchLightning/pytorch-lightning) for training.
For both single-GPU or multiple-GPUs, run:
```shell script
python tools/train.py CONFIG_FILE_PATH
```
4. **Visualize Logs**
TensorBoard logs are saved in `save_dir` which you set in config file.
To visualize tensorboard logs, run:
```shell script
cd
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/export_onnx.py`.
```shell script
python tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}
```
### Run NanoDet in C++ with inference libraries
### ncnn
Please refer to [demo_ncnn](demo_ncnn/README.md).
### OpenVINO
Please refer to [demo_openvino](demo_openvino/README.md).
### MNN
Please refer to [demo_mnn](demo_mnn/README.md).
### Run NanoDet on Android
Please refer to [android_demo](demo_android_ncnn/README.md).
****
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@misc{=nanodet,
title={NanoDet-Plus: Super fast and high accuracy lightweight anchor-free object detection model.},
author={RangiLyu},
howpublished = {\url{https://github.com/RangiLyu/nanodet}},
year={2021}
}
```
****
## 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