Continual Learning without Forgetting - Semantic Segmentation
pytorch implementation of Semantic segmentation using Unet as the network.
Some results
Pascal VOC 2012 / U-Net
- Input -> Ground Truth -> Generated
Currnet
- U-Net
Some details
- I applied different metric algorithms .
- I used augmented dataset. (Currently, I only applied flip operation for augmentation.)
Getting Started
Installation
- Install PyTorch and dependencies from http://pytorch.org
- Install torch torchvision and visdom
pip3 install torch torchvision
pip3 install visdom
- Clone this repo:
git clone https://github.com/LorenzoFramba/Continual-Learning.git
cd Continual-Learning
- Import torch and install cuda
import torch
torch.cuda.is_available()
To train models
python main.py --mode train \
-- n_iters 10000 --train_batch_size 16 val_batch_size 16 \
--h_image_size 256 --w_image_size 256 \
--model_save_path './models' --sample_save_path './samples'
To load the model
--path ../path
--which_epoch 'latest' \
--continue_train \
Dependencies
-
Fully Convolutional Networks for Semantic Segmentation[paper]
Jonathan Long, Evan Shelhamer, Trevor Darrell -
U-Net: Convolutional Networks for Biomedical Image Segmentation [paper]
Olaf Ronneberger, Philipp Fischer, Thomas Brox -
Continual Learning for Dense Labeling of Satellite Images [paper]
Onur Tasar, Yuliya Tarabalka, Pierre Alliez -
Learning a Discriminative Feature Network for Semantic Segmentation [paper]
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang
Contact
If you have any questions about codes, let me know! .