# Scaled-YOLOv4-tensorflow2 **Repository Path**: showtimejmj/Scaled-YOLOv4-tensorflow2 ## Basic Information - **Project Name**: Scaled-YOLOv4-tensorflow2 - **Description**: A Tensorflow2.x implementation of Scaled-YOLOv4 as described in Scaled-YOLOv4: Scaling Cross Stage Partial Network - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2021-07-12 - **Last Updated**: 2021-08-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Scaled-YOLOv4-tensorflow2 [![Python 3.7](https://img.shields.io/badge/Python-3.7-3776AB)](https://www.python.org/downloads/release/python-360/) [![TensorFlow 2.4](https://img.shields.io/badge/TensorFlow-2.4-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0) A Tensorflow2.x implementation of Scaled-YOLOv4 as described in [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://arxiv.org/abs/2011.08036) ## Update Log [2021-07-02]: * Add support for: Exponential moving average decay for variables. Improve mAP from 0.985 to 0.990 on Chess Pieces dataset. [2021-06-29]: Major Features and Improvements: * Add support for: Sharpness-Aware Minimization(SAM_sgd,SAM_adam). Bug Fixes and Changes: * Fix the nan loss error when using adam optimizer * Set default optimizer as SAM_adam * Change default running mode from 'fit' to 'eager mode' [2021-06-27] Add support for: resuming training from checkpoints.

[2021-02-21] Add support for: model.fit(dramatic improvement in GPU utilization); online coco evaluation callback; change default optimizer from sgd to adam

[2021-02-11] Add support for: one-click deployment using tensorflow Serving(very fast)

[2021-01-29] Add support for: mosaic,ssd_random_crop

[2021-01-25] Add support for: ciou loss,hard-nms,DIoU-nms,label_smooth,transfer learning,tensorboard

[2021-01-23] Add support for: scales_x_y/eliminate grid sensitivity,accumulate gradients for using big batch size,focal loss,diou loss

[2021-01-16] Add support for: warmup,Cosine annealing scheduler,Eager mode training with tf.GradientTape,support voc/coco dataset format

[2021-01-10] Add support for: yolov4-tiny,yolov4-large p5/p6/p7,online coco evaluation,multi scale training

## Demo ScaledYOLOv4_p5_detection_result: ![pothole_p5_detection_3.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/pothole_p5_detection_3.png) ![chess_p5_detection.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/chess_p5_detection.png) ScaledYOLOv4_tiny_detection_result: ![safehat_tiny_detection_1.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/safehat_tiny_detection_1.png) ![safehat_tiny_detection_2.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/safehat_tiny_detection_2.png) ## Installation ### 1. Clone project ``` git clone https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2.git cd Scaled-YOLOv4-tensorflow2 ``` ### 2. Install environment * install tesnorflow ( skip this step if it's already installed,test environment:tensorflow 2.4.0) * pip install -r requirements.txt ## Note: I strongly recommend using voc dataset type(default dataset type), because my GPU is old, so coco dataset type is not fully tested. ## Training: * Download Pre-trained p5 coco pretrain models and place it under directory 'pretrained/ScaledYOLOV4_p5_coco_pretrain' :
[https://drive.google.com/file/d/1glOCE3Y5Q5enW3rpVq3SmKDXzaKIw4YL/view?usp=sharing](https://drive.google.com/file/d/1glOCE3Y5Q5enW3rpVq3SmKDXzaKIw4YL/view?usp=sharing)
* Download Pre-trained p6 coco pretrain models and place it under directory 'pretrained/ScaledYOLOV4_p6_coco_pretrain' :
[https://drive.google.com/file/d/1EymbpgiO6VkCCFdB0zSTv0B9yB6T9Fw1/view?usp=sharing](https://drive.google.com/file/d/1EymbpgiO6VkCCFdB0zSTv0B9yB6T9Fw1/view?usp=sharing)
* Download Pre-trained tiny coco pretrain models and place it under directory 'pretrained/ScaledYOLOV4_tiny_coco_pretrain' :
[https://drive.google.com/file/d/1x15FN7jCAFwsntaMwmSkkgIzvHXUa7xT/view?usp=sharing](https://drive.google.com/file/d/1x15FN7jCAFwsntaMwmSkkgIzvHXUa7xT/view?usp=sharing)
* For training on [Pothole dataset](https://public.roboflow.com/object-detection/pothole)(No need to download dataset,it's already included in project):
p5(single scale): ``` python train.py --use-pretrain True --model-type p5 --dataset-type voc --dataset dataset/pothole_voc --num-classes 1 --class-names pothole.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 200 --batch-size 4 --multi-scale 416 --augment ssd_random_crop ``` p5(multi scale): ``` python train.py --use-pretrain True --model-type p5 --dataset-type voc --dataset dataset/pothole_voc --num-classes 1 --class-names pothole.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 200 --batch-size 4 --multi-scale 320,352,384,416,448,480,512 --augment ssd_random_crop ``` * For training on [Chess Pieces dataset](https://public.roboflow.com/object-detection/chess-full)(No need to download dataset,it's already included in project):
tiny(single scale): ``` python train.py --use-pretrain True --model-type tiny --dataset-type voc --dataset dataset/chess_voc --num-classes 12 --class-names chess.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 400 --batch-size 32 --multi-scale 416 --augment ssd_random_crop ``` tiny(multi scale): ``` python train.py --use-pretrain True --model-type tiny --dataset-type voc --dataset dataset/chess_voc --num-classes 12 --class-names chess.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 400 --batch-size 32 --multi-scale 320,352,384,416,448,480,512 --augment ssd_random_crop * For training with SAM_sgd on [Chess Pieces dataset](https://public.roboflow.com/object-detection/chess-full):
``` python train.py --optimizer SAM_sgd --use-pretrain True --model-type tiny --dataset-type voc --dataset dataset/chess_voc --num-classes 12 --class-names chess.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 400 --batch-size 32 --multi-scale 416 --augment ssd_random_crop ``` * For training with ema(Exponential Moving Average) on [Chess Pieces dataset](https://public.roboflow.com/object-detection/chess-full):
``` python train.py --ema True --use-pretrain True --model-type tiny --dataset-type voc --dataset dataset/chess_voc --num-classes 12 --class-names chess.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 400 --batch-size 32 --multi-scale 416 --augment ssd_random_crop ``` ## Tensorboard visualization: * Navigate to [http://0.0.0.0:6006](http://0.0.0.0:6006) ## Evaluation results(GTX2080,mAP@0.5): | model | Chess Pieces | pothole | VOC | COCO | |-----------------------------------------------------|--------------|---------|-------|------| | Scaled-YoloV4-tiny(416) | 0.985 | | | | | Scaled-YoloV4-tiny(416)+ema | 0.990 | | | | | AlexeyAB's YoloV4(416) | | 0.814 | | | | Scaled-YoloV4-p5(416) | | 0.826 | | | * Evaluation on Pothole dataset: ![tensorboard_pothole_p5.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/tensorboard_pothole_p5.png) * Evaluation on chess dataset: ![tensorboard_chess_tiny.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/tensorboard_chess_tiny.png) ## Detection * For detection on Chess Pieces dataset: ``` python3 detect.py --pic-dir images/chess_pictures --model-path output_model/best_model_tiny_0.985/1 --class-names dataset/chess.names --nms-score-threshold 0.1 ``` detection result: ![chess_p5_detection.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/chess_p5_detection.png) * For detection on Pothole dataset: ``` python3 detect.py --pic-dir images/pothole_pictures --model-path output_model/best_model_p5_0.827/1 --class-names dataset/pothole.names --nms-score-threshold 0.1 ``` detection result: ![pothole_p5_detection_2.png](https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2/blob/main/images/pothole_p5_detection_2.png) ## Customzied training * Convert your dataset to Pascal VOC format(you can use labelImg to generate VOC format dataset) * Generate class names file(such as xxx.names) * ``` python train.py --use-pretrain True --model-type p5 --dataset-type voc --dataset your_dataset_root_dir --num-classes num_of_classes --class-names path_of_xxx.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 200 --batch-size 8 --multi-scale 416 --augment ssd_random_crop ``` ## Deployment TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.it include two parts:clients and server, we can run them on one machine.
* **Navigate to deployment directory:** ``` cd deployment/tfserving ``` * **Generate a docker image which contains your trained model (it will take minutes，you only have to run it one time):** ``` ./gen_image --model-dir ScaledYOLOv4-tensorflow2/output_model/pothole/best_model_p5_0.811 ``` * **Deploy model:**
* **Server side**( docker and nvidia-docker installed ): ` ./run_image ` * **Client side**(no need to install tensorflow):
1. install client package ` pip install tfservingclient-1.0.0-cp37-cp37m-manylinux1_x86_64.whl ` 2. predict images ` python demo.py --pic-dir xxxx --class-names xxx.names ` ## References * [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) * [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) * [https://github.com/dmlc/gluon-cv](https://github.com/dmlc/gluon-cv)