Selected Topics in Visual Recognition using Deep Learning

This repository is implementation of homeworks and final project for IOC5008 Selected Topics in Visual Recognition using Deep Learning course in 2021 Fall semester at National Yang Ming Chiao Tung University.

HW1: Caltech-UCSD Birds-200-2011 Classification

In homework 1, we participate a bird classification competition on Codalab. This challenge provided 3,000 training images and 3,033 test images for the fine-grained classification. We rank 21st out of 100 participants at the end. The following table display our scores on the Codalab leaderboard.

method	accuracy
ResNet50	0.71283
ResNet50 + ensemble	0.76525
ResNeXt-101	0.79393
ResNeXt-101 + ensemble	0.81141

HW2: Street View House Numbers Detection

In homework 2, we participate SVHN detection competition on Codalab. This challenge provided 33,402 training images and 13,068 test images for digit detection. We rank 11st out of 83 participants at the end. The following table display our scores on the Codalab leaderboard.

method	mAP@0.5:0.95	speed on P100 GPU (img/s)	speed on K80 GPU (img/s)
Faster-RCNN	0.389141	0.2	X
YOLOv4	0.419870	0.07364	0.13696

HW3: Nuclei Instance Segmentation

In homework 3, we participate nuclei segmentation competition on Codalab. This challenge provided 24 training images with 14,598 nuclei and 6 test images with 2,360 nuclei for instance segmentation. We rank 8st out of 84 participants at the end. The following table display our scores on the Codalab leaderboard.

method	backbone	mAP
Mask R-CNN	ResNet-50-C4	0.244385
Mask R-CNN	ResNet-50-FPN	0.240068
Mask R-CNN	ResNet-101-C4	0.242977
Mask R-CNN	ResNet-101-FPN	0.241530

HW4: Set14 Super-Resolution

In homework 4, we participate super-resolution competition on Codalab. This challenge provided 291 training images with high-resolution images and 14 test images with low-resolution images for super-resolution. We rank 4st out of 85 participants at the end. The following table display our scores on the Codalab leaderboard.

method	PSNR
Bicubic	26.0654
EDSR+	28.0968
SRFBN+	28.4085

Final Project: Ultrasound Nerve Segmentation

In the final project, we participate ultrasound nerve segmentation competition on Kaggle. This challenge provided 5,635 training images and 5,508 test images for semantic segmentation. We rank 7st out of 924 participants at the end. The following table display our scores on Kaggle.

method	backbone	private score
UNet	ResNet34	0.71031
UNet	ResNet50	0.70857
UNet	EfficientNet-b0	0.70233
UNet	EfficientNet-b1	0.72341

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