yolov4-tflite将 YOLO v4、YOLOv3、YOLO tiny .weights 转换为 .pb、.tflite

# tensorflow-yolov4-tflite [](LICENSE) YOLOv4, YOLOv4-tiny Implemented in Tensorflow 2.0. Convert YOLO v4, YOLOv3, YOLO tiny .weights to .pb, .tflite and trt format for tensorflow, tensorflow lite, tensorRT. Download yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT ### Prerequisites * Tensorflow 2.3.0rc0 ### Performance <p align="center"><img src="data/performance.png" width="640"\></p> ### Demo ```bash # Convert darknet weights to tensorflow ## yolov4 python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4 ## yolov4-tiny python save_model.py --weights ./data/yolov4-tiny.weights --output ./checkpoints/yolov4-tiny-416 --input_size 416 --model yolov4 --tiny # Run demo tensorflow python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image ./data/kite.jpg python detect.py --weights ./checkpoints/yolov4-tiny-416 --size 416 --model yolov4 --image ./data/kite.jpg --tiny ``` If you want to run yolov3 or yolov3-tiny change ``--model yolov3`` in command #### Output ##### Yolov4 original weight <p align="center"><img src="result.png" width="640"\></p> ##### Yolov4 tflite int8 <p align="center"><img src="result-int8.png" width="640"\></p> ### Convert to tflite ```bash # Save tf model for tflite converting python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4 --framework tflite # yolov4 python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416.tflite # yolov4 quantize float16 python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416-fp16.tflite --quantize_mode float16 # yolov4 quantize int8 python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416-int8.tflite --quantize_mode int8 --dataset ./coco_dataset/coco/val207.txt # Run demo tflite model python detect.py --weights ./checkpoints/yolov4-416.tflite --size 416 --model yolov4 --image ./data/kite.jpg --framework tflite ``` Yolov4 and Yolov4-tiny int8 quantization have some issues. I will try to fix that. You can try Yolov3 and Yolov3-tiny int8 quantization ### Convert to TensorRT ```bash# yolov3 python save_model.py --weights ./data/yolov3.weights --output ./checkpoints/yolov3.tf --input_size 416 --model yolov3 python convert_trt.py --weights ./checkpoints/yolov3.tf --quantize_mode float16 --output ./checkpoints/yolov3-trt-fp16-416 # yolov3-tiny python save_model.py --weights ./data/yolov3-tiny.weights --output ./checkpoints/yolov3-tiny.tf --input_size 416 --tiny python convert_trt.py --weights ./checkpoints/yolov3-tiny.tf --quantize_mode float16 --output ./checkpoints/yolov3-tiny-trt-fp16-416 # yolov4 python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4.tf --input_size 416 --model yolov4 python convert_trt.py --weights ./checkpoints/yolov4.tf --quantize_mode float16 --output ./checkpoints/yolov4-trt-fp16-416 ``` ### Evaluate on COCO 2017 Dataset ```bash # run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset # preprocess coco dataset cd data mkdir dataset cd .. cd scripts python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl python coco_annotation.py --coco_path ./coco cd .. # evaluate yolov4 model python evaluate.py --weights ./data/yolov4.weights cd mAP/extra python remove_space.py cd .. python main.py --output results_yolov4_tf ``` #### mAP50 on COCO 2017 Dataset | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 | 55.43 | 52.32 | | | YoloV4 | 61.96 | 57.33 | | ### Benchmark ```bash python benchmarks.py --size 416 --model yolov4 --weights ./data/yolov4.weights ``` #### TensorRT performance | YoloV4 416 images/s | FP32 | FP16 | INT8 | |---------------------|----------|----------|----------| | Batch size 1 | 55 | 116 | | | Batch size 8 | 70 | 152 | | #### Tesla P100 | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 FPS | 40.6 | 49.4 | 61.3 | | YoloV4 FPS | 33.4 | 41.7 | 50.0 | #### Tesla K80 | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 FPS | 10.8 | 12.9 | 17.6 | | YoloV4 FPS | 9.6 | 11.7 | 16.0 | #### Tesla T4 | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 FPS | 27.6 | 32.3 | 45.1 | | YoloV4 FPS | 24.0 | 30.3 | 40.1 | #### Tesla P4 | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 FPS | 20.2 | 24.2 | 31.2 | | YoloV4 FPS | 16.2 | 20.2 | 26.5 | #### Macbook Pro 15 (2.3GHz i7) | Detection | 512x512 | 416x416 | 320x320 | |-------------|---------|---------|---------| | YoloV3 FPS | | | | | YoloV4 FPS | | | | ### Traning your own model ```bash # Prepare your dataset # If you want to train from scratch: In config.py set FISRT_STAGE_EPOCHS=0 # Run script: python train.py # Transfer learning: python train.py --weights ./data/yolov4.weights ``` The training performance is not fully reproduced yet, so I recommended to use Alex's [Darknet](https://github.com/AlexeyAB/darknet) to train your own data, then convert the .weights to tensorflow or tflite. ### TODO * [x] Convert YOLOv4 to TensorRT * [x] YOLOv4 tflite on android * [ ] YOLOv4 tflite on ios * [x] Training code * [x] Update scale xy * [ ] ciou * [ ] Mosaic data augmentation * [x] Mish activation * [x] yolov4 tflite version * [x] yolov4 in8 tflite version for mobile ### References * YOLOv4: Optimal Speed and Accuracy of Object Detection [YOLOv4](https://arxiv.org/abs/2004.10934). * [darknet](https://github.com/AlexeyAB/darknet) My project is inspired by these previous fantastic YOLOv3 implementations: * [Yolov3 tensorflow](https://github.com/YunYang1994/tensorflow-yolov3) * [Yolov3 tf2](https://github.com/zzh8829/yolov3-tf2)