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--- |
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language: en |
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tags: |
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- object detection |
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- computer vision |
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- darknet |
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- yolo |
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datasets: |
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- coco |
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- imagenette |
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license: mit |
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thumbnail: https://github.com/hunglc007/tensorflow-yolov4-tflite |
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pipeline_tag: object-detection |
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--- |
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# YOLOv4 |
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YOLO, for "You Only Look Once", is an object detection system in real-time, introduced in [this paper](https://arxiv.org/abs/2004.10934), that recognizes various objects in a single enclosure. It identifies objects more rapidly and more precisely than other recognition systems. Three authors Alexey Bochkovskiy, the Russian developer who built the YOLO Windows version, Chien-Yao Wang, and Hong-Yuan Mark Liao, are accounted for in this work and the entire code is available on [Github](https://github.com/AlexeyAB/darknet). |
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This YOLOv4 library, inspired by previous YOLOv3 implementations here: |
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* [Yolov3 tensorflow](https://github.com/YunYang1994/tensorflow-yolov3) |
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* [Yolov3 tf2](https://github.com/zzh8829/yolov3-tf2)uses Tensorflow 2.0 and is available on this [Github](https://github.com/hunglc007/tensorflow-yolov4-tflite). |
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### Limitations and biases |
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Object-recognition technology has improved drastically in the past few years across the industry, and it is now part of a huge variety of products and services that millions of people worldwide use. However, errors in object-recognition algorithms can stem from the training data used to create the system is geographically constrained and/or that it fails to recognize cultural differences. |
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The COCO dataset used to train yolov4-tflite has been found to have annotation errors on more than 20% of images. Such errors include captions describing people differently based on skin tone and gender expression. This serves as a reminder to be cognizant that these biases already exist and a warning to be careful about the increasing bias that is likely to come with advancements in image captioning technology. |
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### How to use YOLOv4tflite |
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You can use this model to detect objects in an image of choice. Follow the following scripts to implement on your own! |
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```bash |
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# install git lfs |
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git lfs install |
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# if presented with the error "git: 'lfs' is not a git command. See 'git --help'", try running these linux commands: |
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curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash |
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# change directory to base |
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cd .. |
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# install git-lfs |
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sudo apt-get install git-lfs |
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# for message "Git LFS initialized" |
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git lfs install |
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# change directory to yolo_v4_tflite |
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cd ./yolo_v4_tflite |
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# clone this repo into your notebook |
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git clone https://huggingface.co/SamMorgan/yolo_v4_tflite |
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# Run demo tensor flow for an example of how this model works |
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python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image ./data/kite.jpg --output ./test.jpg |
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# Try with your own image |
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python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image <insert path to image of choice> --output <insert path to output location of choice> |
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``` |
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### Evaluate on COCO 2017 Dataset |
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```bash |
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# run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset |
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# preprocess coco dataset |
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cd data |
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mkdir dataset |
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cd .. |
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cd scripts |
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python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl |
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python coco_annotation.py --coco_path ./coco |
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cd .. |
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# evaluate yolov4 model |
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python evaluate.py --weights ./data/yolov4.weights |
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cd mAP/extra |
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python remove_space.py |
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cd .. |
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python main.py --output results_yolov4_tf |
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``` |
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#### mAP50 on COCO 2017 Dataset |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 | 55.43 | 52.32 | | |
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| YoloV4 | 61.96 | 57.33 | | |
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### Benchmark |
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```bash |
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python benchmarks.py --size 416 --model yolov4 --weights ./data/yolov4.weights |
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``` |
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#### TensorRT performance |
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| YoloV4 416 images/s | FP32 | FP16 | INT8 | |
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|---------------------|----------|----------|----------| |
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| Batch size 1 | 55 | 116 | | |
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| Batch size 8 | 70 | 152 | | |
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#### Tesla P100 |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 FPS | 40.6 | 49.4 | 61.3 | |
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| YoloV4 FPS | 33.4 | 41.7 | 50.0 | |
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#### Tesla K80 |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 FPS | 10.8 | 12.9 | 17.6 | |
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| YoloV4 FPS | 9.6 | 11.7 | 16.0 | |
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#### Tesla T4 |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 FPS | 27.6 | 32.3 | 45.1 | |
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| YoloV4 FPS | 24.0 | 30.3 | 40.1 | |
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#### Tesla P4 |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 FPS | 20.2 | 24.2 | 31.2 | |
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| YoloV4 FPS | 16.2 | 20.2 | 26.5 | |
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#### Macbook Pro 15 (2.3GHz i7) |
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| Detection | 512x512 | 416x416 | 320x320 | |
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|-------------|---------|---------|---------| |
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| YoloV3 FPS | | | | |
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| YoloV4 FPS | | | | |
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### Traning your own model |
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```bash |
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# Prepare your dataset |
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# If you want to train from scratch: |
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In config.py set FISRT_STAGE_EPOCHS=0 |
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# Run script: |
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python train.py |
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# Transfer learning: |
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python train.py --weights ./data/yolov4.weights |
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``` |
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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. |
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### References |
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* YOLOv4: Optimal Speed and Accuracy of Object Detection [YOLOv4](https://arxiv.org/abs/2004.10934). |
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* [darknet](https://github.com/AlexeyAB/darknet) |
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