yolov1's Introduction

This project aims to implement YOLOV1 algorithm

Introduction

YOLOv1 is new approach to object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. YOLO is extremely fast, YOLO sees the entire image during training and test time so it implicitly encodes contextual information about classes as well as their appearance.

Update news

2023/07/05: Update DIoU, CIoU loss
2023/07/03: Update GIoU loss
2023/07/01: Update model weights Resnet18, Resnet34, Resnet50

Experiment Table

Backbone	Dataset	Training dataset	Valid dataset	Image size	mAP	mAP_50	mAP_75
Original paper	PASCAL VOC	trainval 2007+2012	test2007	448x448	--	0.63	--
ResNet18 + GIoU loss	PASCAL VOC	trainval 2007+2012	test2007	448x448	0.41	0.64	0.44
ResNet50 + GIoU loss	PASCAL VOC	trainval 2007+2012	test2007	448x448	0.46	0.69	0.49

Dataset

Download Pascal VOC train+val 2012+2007
Download Pascal VOC test 2007

Put all images, annotations, txt files in folder dataset/VOC folder as following:

├── VOC
    ├── images
        ├── trainval2007
            ├── 000005.jpg
            ├── 000007.jpg
        ├── trainval2012
        ├── test2007
    ├── images_id
        ├── trainval2007.txt
        ├── trainval2012.txt
        ├── test2007.txt
    ├── labels
        ├── trainval2007
            ├── 000005.xml
            ├── 000007.xml
        ├── trainval2012
        ├── trainval2007

Training

python -m src.train --model_type resnet18/resnet34/resnet50 --resume resume_most_recent_training

Evaluate

python -m src.eval --model_type resnet18/resnet34/resnet50 --weight_type path_to_weight_best.pt

Prediction

python -m src.predict --model_type resnet18/resnet34/resnet50 --weight_path path_to_weight_best.pt --input_folder path_input_folder --output_folder path_to_output_folder --conf_thresh confidence_threshold --iou_thresh iou threshold

Recommend Projects