AKASH2907 / autonomous_driving_detection

Indian Driving Dataset Object Detection

idd_driving_detection

Indian Driving Dataset Road Objects Detection

workflow

1. Convert the images and annotations names to '0.xml/0.jpg', '1.xml/1.jpg' and so on.
2. Use xml_csv.py file to convert the xml format to csv/txt format.

Codes to work upon

Look for repo containing cityscapes dataset and run the codes anyhow Cityscapes have void classes look for it

1. divamgupta github repo -> Try segnet and all rest of the models
2. qubvel segmentation models
3. Efficient Net
4. PSP Net
5. Deep Lab V3
6. kNN -> Nearest pixel -> Class identification + Dealing with void pixels

Problems Currently facing

1. How to deal with unlabeled pixels in image segmentation?
2. Dealing with instance labels in the dataset

Concepts to look upon

1. FCN-8, 16, 32 difference

1st set of results Validation:

Level 1 Scores	mIoU
drivable	0.7845120106595769
non-drivable	0.23014790726948495
living-things	0.3588385523008605
vehicles	0.6218017694882831
road-side-objs	0.34466576972739665
far-objects	0.6662080565682629
sky	0.8938274671391853
mIoU	0.4875001916441313

[0.78466189 0.23014791 0.35885548 0.6218409 0.34473394 0.66637605 0.89399533]

mIoU: 55.723021395073424

Submissions

S. No.	Architecture	mIoU	Comments
1	Res50+UNet	0.404	10 epochs + residual pixels==0

Solved

• Change the intensity of particular values
• Change the name to label.png
• Directories make
• Test folder some images are not present -> 5 images ID find out