# :zap:FastestDet:zap: ![image](https://github.com/dog-qiuqiu/FastestDet/blob/main/data/data.png) * ***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*** # Evaluating indicator/Benchmark Network|COCO mAP(0.5)|Resolution|Run Time(4xCore)|Run Time(1xCore)|FLOPs(G)|Params(M) :---:|:---:|:---:|:---:|:---:|:---:|:---: [Yolo-FastestV1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40 %|320X320|26.60 ms|75.74 ms|0.252|0.35M [Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|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](https://github.com/Tencent/ncnn)*** # Improvement * Anchor-Free * Single scale detector head * Cross grid multiple candidate targets * Dynamic positive and negative sample allocation # How to use ## Dependent installation * PiP(Note pytorch CUDA version selection) ``` pip install -r requirements.txt ``` ## Test * Picture test ``` python3 test.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg ```
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## How to train ### Building data sets(The dataset is constructed in the same way as darknet yolo) * 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.jpg ``` val.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 ``` ### Build the training .yaml configuration file * Reference./configs/config.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 ``` ### Train * Perform training tasks ``` python3 train.py --yaml configs/config.yaml ``` ### Evaluation * Calculate map evaluation ``` python3 eval.py --yaml configs/config.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 ``` # Deploy ## NCNN * Waiting for update # Citation * 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} } ``` # Reference * https://github.com/Tencent/ncnn