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.
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 |
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 |
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 |
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 |
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 |