CMPUT Course Project

Author: Leen Alzebdeh

Summary

I customize YOLOv5 and U-Net on a MNIST Double Digits RGB (MNISTDD-RGB) for a train-valid-test split dataset which was provided from the course, more details below.

Object Detection on MNIST Double Digits RGB (MNISTDD-RGB)

Project page: https://leen-alzebdeh.github.io/projects/328_detection/

Dataset consists of:

• input: numpy array of numpy arrays which each represent pixels in the image, shape: number of samples, 12288 (flattened 64x64x3 images)
• output:
  • classes: numpy array of numpy arrays which each represents the classes in the corresponding image, shape: number of samples, 2
  • prediction boxes: numpy array of numpy arrays which each represents the bounding boxes in the corresponding image, format: [y_min, x_min, y_max, x_max], shape: number of samples, 2, 4

I use YOLOv5 for object detection. I achieve a classification score of 98.786% and an IOU score of 63.371%, resulting in an overall score of 81.078%.

Semantic Image Segmentation on MNIST Double Digits RGB (MNISTDD-RGB)

Project page: https://leen-alzebdeh.github.io/projects/328_segmentation/

Dataset consists of:

• input: numpy array of numpy arrays which each represent pixels in the image, shape: number of samples, 12288 (flattened 64x64x3 images)
• output:
  • segementations: numpy array of numpy arrays which each represents the labels in the corresponding image, shape: number of samples, 4096 (flattened 64x64)

I customized a U-Net model for image segmentation. I achieve an accuracy of 87%.

References

Pytorch. PyTorch. (n.d.). Retrieved May 2, 2023, from https://pytorch.org/hub/ultralytics_yolov5/

Kathuria, A. (2023, April 10). How to train Yolo V5 on a custom dataset. Paperspace Blog. Retrieved May 2, 2023, from https://blog.paperspace.com/train-yolov5-custom-data/

Solawetz, J. (2020, September 29). How to train a custom object detection model with Yolo V5. Medium. Retrieved May 2, 2023, from https://towardsdatascience.com/how-to-train-a-custom-object-detection-model-with-yolo-v5-917e9ce13208

(2017).Pytorch-Unet, from https://github.com/milesial/Pytorch-UNet

I used the course's code templates from:

• Assignment 7: Object Detection/predict.py from A7_submission and Object detection/predict.py from A7_main
• Assignment 8: Image Segmentation/predict.py from A8_submission and Image Segmentation/predict.py from A8_main