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

Object Depth via Motion and Detection Dataset

Vision-Language Pre-training for Image Captioning and Question Answering

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

Gesture-controlled Video Game. Just swing your finger and play the game without touching your PC

JAX + dataclasses

Teaches a student network from the knowledge obtained via training of a larger teacher network

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Dogs classification with Deep Metric Learning using some popular losses

QueryDet: Cascaded Sparse Query for Accelerating High-Resolution SmallObject Detection

Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

A multilingual version of MS MARCO passage ranking dataset

Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis in JAX

Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squares.

Code for Mining the Benefits of Two-stage and One-stage HOI Detection

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Uses Open AI Gym environment to create autonomous cryptocurrency bot to trade cryptocurrencies.

The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.