U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

Last update: Jan 06, 2022

Overview

U-Net Implementation

By Christopher Ley

This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

This data set is a Binary Segmentation exercise of ~400 test images of cars from various angles such as those shown here:

Initial implementation for Binary Segmentation

The implementation performs almost as the winners of the competition (Dice: 0.9926 vs 0.99733) after only 5 epoch and we would expect the results to be as good as the winners using this architecture with more training and a little tweaking of the training hyper-parameters.

Here are the scores for training over 5 epochs by running:

(DeepLearning): python3 train.py

Training Results

0%|          | 0/540 [00:00<?, ?it/s]Accuracy: 103298971/467927040 = 22.08%
Dice score: 0.36127230525016785
100%|██████████| 540/540 [05:59<00:00,  1.50it/s, loss=0.0949]
==> Saving Checkpoint to: ./checkpoints/checkpoint_2022-01-06_12:39_epoch_0.pth.tar
Accuracy: 460498379/467927040 = 98.41%
Dice score: 0.9652246236801147
100%|██████████| 540/540 [05:59<00:00,  1.50it/s, loss=0.0469]
==> Saving Checkpoint to: ./checkpoints/checkpoint_2022-01-06_12:48_epoch_1.pth.tar
Accuracy: 461809183/467927040 = 98.69%
Dice score: 0.9711439609527588
100%|██████████| 540/540 [05:56<00:00,  1.51it/s, loss=0.0283]
==> Saving Checkpoint to: ./checkpoints/checkpoint_2022-01-06_12:56_epoch_2.pth.tar
Accuracy: 465675737/467927040 = 99.52%
Dice score: 0.9891990423202515
100%|██████████| 540/540 [06:00<00:00,  1.50it/s, loss=0.0194]
==> Saving Checkpoint to: ./checkpoints/checkpoint_2022-01-06_13:04_epoch_3.pth.tar
Accuracy: 465397979/467927040 = 99.46%
Dice score: 0.9878408908843994
100%|██████████| 540/540 [06:00<00:00,  1.50it/s, loss=0.0142]
==> Saving Checkpoint to: ./checkpoints/checkpoint_2022-01-06_13:12_epoch_4.pth.tar
Accuracy: 466399501/467927040 = 99.67%
Dice score: 0.9926225543022156

And an example of the output vs the ground truth of the validation set, I removed whole makes for the validation set, all 16 angles, the network had never seen this particular make from any angle.

Ground Truth

Prediction

Although limited in scope (binary segmentation for only cars), this architecture performs well with multiclass segmentation, I extended this to apply segmentation to the NYUv2 which is a multiclass objective, with little modification to the above code.

I will clean this up and upload the results and modifications soon!

U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

Related tags

Overview

U-Net Implementation

By Christopher Ley

Initial implementation for Binary Segmentation

Training Results

Ground Truth

Prediction

Owner

Christopher Ley

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Reproduces ResNet-V3 with pytorch

Submanifold sparse convolutional networks

Deep Learning GPU Training System

Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

Compare GAN code.

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Video Background Music Generation with Controllable Music Transformer (ACM MM 2021 Oral)

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

A modular application for performing anomaly detection in networks

Code, Models and Datasets for OpenViDial Dataset

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence) and pre-trained model on ImageNet dataset