- Faster! Stronger! Simpler!
- It has better single core reasoning performance and simpler feature map post-processing than Yolo-fastest
- In the ARM CPU of RK3568, the single core reasoning performance is 50% higher than Yolo-fastest
- The coco evaluation index increased by 3.8% compared with the map0.5 of Yolo-fastest
- 算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604
| Network | COCO mAP(0.5) | Resolution | Run Time(4xCore) | Run Time(1xCore) | FLOPs(G) | Params(M) |
|---|---|---|---|---|---|---|
| Yolo-FastestV1.1 | 24.40 % | 320X320 | 26.60 ms | 75.74 ms | 0.252 | 0.35M |
| Yolo-FastestV2 | 24.10 % | 352X352 | 23.8 ms | 68.9 ms | 0.212 | 0.25M |
| FastestDet | 27.8% | 512X512 | 21.51ms | 34.62ms | * | 0.25M |
- Test platform RK3568 CPU，Based on NCNN
- 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 | 34.62ms | 21.51ms |
| AMD | R5-5600(X86-cpu) | Linux(amd64) | ncnn | 2.16ms | 1.73ms |
| Intel | i7-8700(X86-cpu) | Linux(amd64) | ncnn | 5.21ms | 4.73ms |
- PiP(Note pytorch CUDA version selection)
pip install -r requirements.txt
- Picture test
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_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 0.972 -
The 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.txt -
Generate 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.jpg -
Generate 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: 512 # The width of the model input image
INPUT_HEIGHT: 512 # 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\:0.278_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 ] = 0.140 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.128 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.225 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359
- You can export .onnx by adding the --onnx option when executing test.py
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg --onnx
- 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}
}