2022.7.14:Optimize loss, adopt IOU aware based on smooth L1, and the AP is significantly increased by 0.7
- Faster! Stronger! Simpler!
- It has better performance and simpler feature map post-processing than Yolo-fastest
- The performance is 10% higher than Yolo-fastest
- The coco evaluation index increased by 1.2% compared with the map0.5 of Yolo-fastestv2
- 算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604
| Network | mAPval 0.5 | mAPval 0.5:0.95 | Resolution | Run Time(4xCore) | Run Time(1xCore) | Params(M) |
|---|---|---|---|---|---|---|
| yolov5s | 56.8% | 37.4% | 640X640 | 395.31ms | 1139.16ms | 7.2M |
| yolov6n | - | 30.8% | 416X416 | 109.24ms | 445.44ms | 4.3M |
| yolox-nano | - | 25.8% | 416X416 | 76.31ms | 191.16ms | 0.91M |
| nanodet_m | - | 20.6% | 320X320 | 49.24ms | 160.35ms | 0.95M |
| yolo-fastestv1.1 | 24.40% | - | 320X320 | 26.60ms | 75.74ms | 0.35M |
| yolo-fastestv2 | 24.10% | - | 352X352 | 23.8ms | 68.9ms | 0.25M |
| FastestDet | 25.3% | 13.0% | 352X352 | 23.51ms | 70.62ms | 0.24M |
- Test platform Radxa Rock3A RK3568 ARM Cortex-A55 CPU，Based on NCNN
- CPU lock frequency 2.0GHz
- Anchor-Free
- Single scale detector head
- Cross grid multiple candidate targets
- Dynamic positive and negative sample allocation
| Equipment | Computing backend | System | Framework | Run time(Single core) | Run time(Multi core) |
|---|---|---|---|---|---|
| Radxa rock3a | RK3568(arm-cpu) | Linux(aarch64) | ncnn | 70.62ms | 23.51ms |
| Radxa rock3a | RK3568(NPU) | Linux(aarch64) | rknn | 28ms | - |
| Qualcomm | Snapdragon 835(arm-cpu) | Android(aarch64) | ncnn | 32.34ms | 16.24ms |
| Intel | i7-8700(X86-cpu) | Linux(amd64) | ncnn | 4.51ms | 4.33ms |
- PiP(Note pytorch CUDA version selection)
pip install -r requirements.txt
- Picture test
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg
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The format of the data set is the same as that of Darknet Yolo, Each image corresponds to a .txt label file. The label format is also based on Darknet Yolo's data set label format: "category cx cy wh", where category is the category subscript, cx, cy are the coordinates of the center point of the normalized label box, and w, h are the normalized label box The width and height, .txt label file content example as follows:
11 0.344192634561 0.611 0.416430594901 0.262 14 0.509915014164 0.51 0.974504249292 0.972The image and its corresponding label file have the same name and are stored in the same directory. The data file structure is as follows:
. ├── train │ ├── 000001.jpg │ ├── 000001.txt │ ├── 000002.jpg │ ├── 000002.txt │ ├── 000003.jpg │ └── 000003.txt └── val ├── 000043.jpg ├── 000043.txt ├── 000057.jpg ├── 000057.txt ├── 000070.jpg └── 000070.txtGenerate a dataset path .txt file, the example content is as follows：
train.txt
/home/qiuqiu/Desktop/dataset/train/000001.jpg /home/qiuqiu/Desktop/dataset/train/000002.jpg /home/qiuqiu/Desktop/dataset/train/000003.jpgval.txt
/home/qiuqiu/Desktop/dataset/val/000070.jpg /home/qiuqiu/Desktop/dataset/val/000043.jpg /home/qiuqiu/Desktop/dataset/val/000057.jpgGenerate the .names category label file, the sample content is as follows:
category.names
person bicycle car motorbike ...The directory structure of the finally constructed training data set is as follows:
. ├── category.names # .names category label file ├── train # train dataset │ ├── 000001.jpg │ ├── 000001.txt │ ├── 000002.jpg │ ├── 000002.txt │ ├── 000003.jpg │ └── 000003.txt ├── train.txt # train dataset path .txt file ├── val # val dataset │ ├── 000043.jpg │ ├── 000043.txt │ ├── 000057.jpg │ ├── 000057.txt │ ├── 000070.jpg │ └── 000070.txt └── val.txt # val dataset path .txt file
- Reference./configs/coco.yaml
DATASET: TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt" # Train dataset path .txt file VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt" # Val dataset path .txt file NAMES: "dataset/coco128/coco.names" # .names category label file MODEL: NC: 80 # Number of detection categories INPUT_WIDTH: 352 # The width of the model input image INPUT_HEIGHT: 352 # The height of the model input image TRAIN: LR: 0.001 # Train learn rate THRESH: 0.25 # ？？？？ WARMUP: true # Trun on warm up BATCH_SIZE: 64 # Batch size END_EPOCH: 350 # Train epichs MILESTIONES: # Declining learning rate steps - 150 - 250 - 300
- Perform training tasks
python3 train.py --yaml configs/coco.yaml
- Calculate map evaluation
python3 eval.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth - COCO2017 evaluation
creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=30.85s). Accumulating evaluation results... DONE (t=4.97s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.119 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.043 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372
- You can export .onnx by adding the --onnx option when executing test.py
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --onnx
- You can export .pt by adding the --torchscript option when executing test.py
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --torchscript
- Need to compile ncnn and opencv in advance and modify the path in build.sh
cd example/ncnn/ sh build.sh ./FastestDet
- You can learn about the pre and post-processing methods of FastestDet in this Sample
cd example/onnx-runtime pip install onnx-runtime python3 runtime.py
- If you find this project useful in your research, please consider cite:
@misc{=FastestDet, title={FastestDet: Ultra lightweight anchor-free real-time object detection algorithm.}, author={xuehao.ma}, howpublished = {\url{https://github.com/dog-qiuqiu/FastestDet}}, year={2022} }