Dataset Preparation The base data directory should consist of the following files: "images" folder - containing the images for train, test and val "labels" folder - containing the labels for train, test and val in .txt yolo format. Each image has seperate file.

	"class.txt" - Txt file with class names (Input to the conv_xml_to_txt.py file)

	Running conv_xml_to_txt.py at the base data directory:
	"train/txt" - generate txt files containing labels(class,xyxy) from original xml labels(Input)
	"val/txt" - generate txt files containing labels(class,xyxy) from original xml labels(Input)
	"train/txt" - generate txt files containing labels(class,xyxy) from original xml labels(Input)

Generated custom dataset should be in the format :

custom_dataset
├── images
│   ├── train
│   │   ├── train0.jpg
│   │   └── train1.jpg
│   ├── val
│   │   ├── val0.jpg
│   │   └── val1.jpg
│   └── test
│       ├── test0.jpg
│       └── test1.jpg
└── labels
   	    ├── train
	    │   ├── train0.txt
	    │   └── train1.txt
	    ├── val
	    │   ├── val0.txt
	    │   └── val1.txt
	    └── test
    	├── test0.txt
    	└── test1.txt

TRAIN

- python tools\train.py --batch 16 --conf E:\IISc\Object_detection\YOLOv6\YOLOv6-main\configs\yolov6s.py --data E:\IISc\Object_detection\YOLOv6\YOLOv6-main\data\dataset.yaml --device 0 --epochs 25 --eval-final-only

saved weights
- \tools\runs\train\exp\

(NOTE: DELETE the train.cache, val.cache, test.cache files generated after each run in dataset\labels)

Evaluate : (For final mAP calculation on custom dataset)

Set val variable value in data\custom.yaml as- 
E:\\IISc\\Object_detection\\IDD\\backup\\images\\test

- python \tools\eval.py --data E:\IISc\Object_detection\YOLOv6\YOLOv6-main\data\dataset.yaml --batch 2 --weights E:\IISc\Object_detection\YOLOv6\YOLOv6-main\weights\best_ckpt.pt --task val

(NOTE: DELETE the train.cache, val.cache, test.cache files generated after each run in dataset\labels)

EVALUATION : (Use xyxy format labels for label and image paths in main() of Evaluation.py) - \Evaluation\Evaluation.py - Generate csv files with results (Refer Evaluation\Readme_evaluation.txt for details)

(NOTE: DELETE the train.cache, val.cache, test.cache files generated after each run in dataset\labels)