这是一个基于yolo-fastest模型的小车，主控是art-pi开发板，使用了rt thread操作系统 该小车能够识别特定种

# TF Keras YOLOv4/v3/v2 Modelset [](LICENSE) ## Introduction A general YOLOv4/v3/v2 object detection pipeline inherited from [keras-yolo3-Mobilenet](https://github.com/Adamdad/keras-YOLOv3-mobilenet)/[keras-yolo3](https://github.com/qqwweee/keras-yolo3) and [YAD2K](https://github.com/allanzelener/YAD2K). Implement with tf.keras, including data collection/annotation, model training/tuning, model evaluation and on device deployment. Support different architecture and different technologies: #### Backbone - [x] CSPDarknet53 - [x] Darknet53/Tiny Darknet - [x] Darknet19 - [x] MobilenetV1 - [x] MobilenetV2 - [x] MobilenetV3(Large/Small) - [x] PeleeNet ([paper](https://arxiv.org/abs/1804.06882)) - [x] GhostNet ([paper](https://arxiv.org/abs/1911.11907)) - [x] EfficientNet - [x] Xception - [x] VGG16 #### Head - [x] YOLOv4 (Lite) - [x] Tiny YOLOv4 (Lite, no-SPP, unofficial) - [x] YOLOv3 (Lite, SPP) - [x] YOLOv3 Nano ([paper](https://arxiv.org/abs/1910.01271)) (unofficial) - [x] Tiny YOLOv3 (Lite) - [x] YOLOv2 (Lite) - [x] Tiny YOLOv2 (Lite) #### Loss - [x] YOLOv3 loss - [x] YOLOv2 loss - [x] Binary focal classification loss - [x] Softmax focal classification loss - [x] GIoU localization loss - [x] DIoU localization loss ([paper](https://arxiv.org/abs/1911.08287)) - [x] Binary focal loss for objectness (experimental) - [x] Label smoothing for classification loss #### Postprocess - [x] Numpy YOLOv3/v2 postprocess implementation - [x] TFLite/MNN C++ YOLOv3/v2 postprocess implementation - [x] tf.keras batch-wise YOLOv3/v2 postprocess layer - [x] DIoU-NMS bounding box postprocess (numpy/C++) - [x] SoftNMS bounding box postprocess (numpy) - [x] Eliminate grid sensitivity (numpy/C++, from [YOLOv4](https://arxiv.org/abs/2004.10934)) - [x] WBF(Weighted-Boxes-Fusion) bounding box postprocess (numpy) ([paper](https://arxiv.org/abs/1910.13302)) - [x] Cluster NMS family (Fast/Matrix/SPM/Weighted) bounding box postprocess (numpy) ([paper](https://arxiv.org/abs/2005.03572)) #### Train tech - [x] Transfer training from imagenet - [x] Singlescale image input training - [x] Multiscale image input training - [x] Dynamic learning rate decay (Cosine/Exponential/Polynomial/PiecewiseConstant) - [x] Weights Average policy for optimizer (EMA/SWA/Lookahead, valid for TF-2.x with tfa) - [x] Mosaic data augmentation (from [YOLOv4](https://arxiv.org/abs/2004.10934)) - [x] GridMask data augmentation ([paper](https://arxiv.org/abs/2001.04086)) - [x] Multi anchors for single GT (from [YOLOv4](https://arxiv.org/abs/2004.10934)) - [x] Pruned model training (only valid for TF 1.x) - [x] Multi-GPU training with SyncBatchNorm support (valid for TF-2.2 and later) #### On-device deployment - [x] Tensorflow-Lite Float32/UInt8 model inference - [x] MNN Float32/UInt8 model inference ## Quick Start 1. Install requirements on Ubuntu 16.04/18.04: ``` # apt install python3-opencv # pip install Cython # pip install -r requirements.txt ``` 2. Download Related Darknet/YOLOv2/v3/v4 weights from [YOLO website](http://pjreddie.com/darknet/yolo/) and [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). 3. Convert the Darknet YOLO model to a Keras model. 4. Run YOLO detection on your image or video, default using Tiny YOLOv3 model. ``` # wget -O weights/darknet53.conv.74.weights https://pjreddie.com/media/files/darknet53.conv.74 # wget -O weights/darknet19_448.conv.23.weights https://pjreddie.com/media/files/darknet19_448.conv.23 # wget -O weights/yolov3.weights https://pjreddie.com/media/files/yolov3.weights # wget -O weights/yolov3-tiny.weights https://pjreddie.com/media/files/yolov3-tiny.weights # wget -O weights/yolov3-spp.weights https://pjreddie.com/media/files/yolov3-spp.weights # wget -O weights/yolov2.weights http://pjreddie.com/media/files/yolo.weights # wget -O weights/yolov2-voc.weights http://pjreddie.com/media/files/yolo-voc.weights # wget -O weights/yolov2-tiny.weights https://pjreddie.com/media/files/yolov2-tiny.weights # wget -O weights/yolov2-tiny-voc.weights https://pjreddie.com/media/files/yolov2-tiny-voc.weights ### manually download csdarknet53-omega_final.weights from https://drive.google.com/open?id=18jCwaL4SJ-jOvXrZNGHJ5yz44g9zi8Hm # wget -O weights/yolov4.weights https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights # python tools/model_converter/convert.py cfg/yolov3.cfg weights/yolov3.weights weights/yolov3.h5 # python tools/model_converter/convert.py cfg/yolov3-tiny.cfg weights/yolov3-tiny.weights weights/yolov3-tiny.h5 # python tools/model_converter/convert.py cfg/yolov3-spp.cfg weights/yolov3-spp.weights weights/yolov3-spp.h5 # python tools/model_converter/convert.py cfg/yolov2.cfg weights/yolov2.weights weights/yolov2.h5 # python tools/model_converter/convert.py cfg/yolov2-voc.cfg weights/yolov2-voc.weights weights/yolov2-voc.h5 # python tools/model_converter/convert.py cfg/yolov2-tiny.cfg weights/yolov2-tiny.weights weights/yolov2-tiny.h5 # python tools/model_converter/convert.py cfg/yolov2-tiny-voc.cfg weights/yolov2-tiny-voc.weights weights/yolov2-tiny-voc.h5 # python tools/model_converter/convert.py cfg/darknet53.cfg weights/darknet53.conv.74.weights weights/darknet53.h5 # python tools/model_converter/convert.py cfg/darknet19_448_body.cfg weights/darknet19_448.conv.23.weights weights/darknet19.h5 # python tools/model_converter/convert.py cfg/csdarknet53-omega.cfg weights/csdarknet53-omega_final.weights weights/cspdarknet53.h5 ### make sure to reorder output tensors for YOLOv4 cfg and weights file # python tools/model_converter/convert.py --yolo4_reorder cfg/yolov4.cfg weights/yolov4.weights weights/yolov4.h5 ### Scaled YOLOv4 ### manually download yolov4-csp.weights from https://drive.google.com/file/d/1NQwz47cW0NUgy7L3_xOKaNEfLoQuq3EL/view?usp=sharing # python tools/model_converter/convert.py --yolo4_reorder cfg/yolov4-csp_fixed.cfg weights/yolov4-csp.weights weights/scaled-yolov4-csp.h5 ### Yolo-Fastest # wget -O weights/yolo-fastest.weights https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.0_coco/yolo-fastest.weights # wget -O weights/yolo-fastest-xl.weights https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.0_coco/yolo-fastest-xl.weights # python tools/model_converter/convert.py cfg/yolo-fastest.cfg weights/yolo-fastest.weights weights/yolo-fastest.h5 # python tools/model_converter/convert.py cfg/yolo-fastest-xl.cfg weights/yolo-fastest-xl.weights weights/yolo-fastest-xl.h5 # python yolo.py --image # python yolo.py --input=<your video file> ``` For other model, just do in a similar way, but specify different model type, weights path and anchor path with `--model_type`, `--weights_path` and `--anchors_path`. Image detection sample: <p align="center"> <img src="assets/dog_inference.jpg"> <img src="assets/kite_inference.jpg"> </p> ## Guide of train/evaluate/demo ### Train 1. Generate train/val/test annotation file and class names file. Data annotation file format: * One row for one image in annotation file; * Row format: `image_file_path box1 box2 ... boxN`; * Box format: `x_min,y_min,x_max,y_max,class_id` (no space). * Here is an example: ``` path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3 path/to/img2.jpg 120,300,250,600,2 ... ``` 1. For VOC style dataset, you can use [voc_annotation.py](https://github.com/david8862/keras-YOLOv3-model-set/blob/master/tools/dataset_converter/voc_annotation.py) to convert original dataset to our annotation file: ``` # cd tools/dataset_converter/ && python voc_annotation.py -h usage: voc_annotation.py [-h] [--dataset_path DATASET_PATH] [--year YEAR] [--set SET] [--output_path OUTPUT_PATH] [--classes_path CLASSES_PATH] [--include_difficult] [--include_no_obj] convert PascalVOC