To create a social distancing detector using Yolo v4 detector to detect humans.
Get a bird's eye view of the location and map humans detected by yolov4 onto it. Then, calculate Euclidean distance between the points to check for humans that are too close to each other.
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Download yolov4 weight
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Added gitignore file to ignore env folder
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Create virtual env
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Pip install requirements-gpu.txt
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Run save_model.py to convert yolov4.weight darknet file to tensorflow pb files: python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4
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Edit draw_bbox function in utils.py to ensure only humans are detected
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Run detectvideo.py to see how yolov4 does object detection: python detectvideo.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --video ./data/pedestrians.mp4
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Run Social Distancing Detector: python social_distancing.py -m ./checkpoints/yolov4-416 -d True -i ./data/pedestrians.mp4 -oc detections_camera_view.avi -ob detections_birds_eye_view.avi
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
- Tensorflow 2.3.0rc0
# 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
# 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
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
# 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
Detection | 512x512 | 416x416 | 320x320 |
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YoloV3 | 55.43 | 52.32 | |
YoloV4 | 61.96 | 57.33 |
python benchmarks.py --size 416 --model yolov4 --weights ./data/yolov4.weights
YoloV4 416 images/s | FP32 | FP16 | INT8 |
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Batch size 1 | 55 | 116 | |
Batch size 8 | 70 | 152 |
Detection | 512x512 | 416x416 | 320x320 |
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YoloV3 FPS | 40.6 | 49.4 | 61.3 |
YoloV4 FPS | 33.4 | 41.7 | 50.0 |
Detection | 512x512 | 416x416 | 320x320 |
---|---|---|---|
YoloV3 FPS | 10.8 | 12.9 | 17.6 |
YoloV4 FPS | 9.6 | 11.7 | 16.0 |
Detection | 512x512 | 416x416 | 320x320 |
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YoloV3 FPS | 27.6 | 32.3 | 45.1 |
YoloV4 FPS | 24.0 | 30.3 | 40.1 |
Detection | 512x512 | 416x416 | 320x320 |
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YoloV3 FPS | 20.2 | 24.2 | 31.2 |
YoloV4 FPS | 16.2 | 20.2 | 26.5 |
Detection | 512x512 | 416x416 | 320x320 |
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YoloV3 FPS | |||
YoloV4 FPS |
# 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 to train your own data, then convert the .weights to tensorflow or tflite.
- Convert YOLOv4 to TensorRT
- YOLOv4 tflite on android
- YOLOv4 tflite on ios
- Training code
- Update scale xy
- ciou
- Mosaic data augmentation
- Mish activation
- yolov4 tflite version
- yolov4 in8 tflite version for mobile
My project is inspired by these previous fantastic YOLOv3 implementations: