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Author: priya-dwivedi

segmentation_with_RGBD/README.md

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+# FuseNet
+
+This repository contains PyTorch implementation of FuseNet-SF5 architecture from the paper
+[FuseNet: incorporating depth into semantic segmentation via fusion-based CNN architecture](https://pdfs.semanticscholar.org/9360/ce51ec055c05fd0384343792c58363383952.pdf). 
+
+
+## Installation
+Prerequisites:
+- python 3.6
+- Nvidia GPU + CUDA cuDNN
+```
+## Datasets 
+
+### [NYU-Depth V2](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
+- Simply, create a directory named datasets in the main project directory and in datasets directory download the preprocessed dataset, in HDF5 format, with 40 semantic-segmentation and 10 scene classes here: [train + test set](https://vision.in.tum.de/webarchive/hazirbas/fusenet-pytorch/nyu/nyu_class_10_db.h5)
+- Preprocessed dataset contains 1449 (train: 795, test: 654) RGB-D images with 320x240 resolution, their semantic-segmentation and scene-type annotations.
+- Depth image values have been normalized so that they fall into 0-255 range. 
+
+## Training
+- To train FuseNet, run `fusenet_train.py` by providing the path of the dataset. 
+- If you would like to train a FuseNet model with the classification head, provide `--use_class True`
+- Example training commands can be found below.
+
+### Training from scratch
+
+```
+python fusenet_train.py --dataroot ./datasets/nyu_class_10_db.h5 --batch_size 8 --lr 0.005 --num_epochs 125
+```
+
+### Resuming training from a checkpoint
+
+python fusenet_train.py --dataroot ./datasets/nyu_class_10_db.h5 --resume_train True --batch_size 8 \
+                        --load_checkpoint ./checkpoints/may27_first_run/nyu/best_model.pth.tar --lr 0.005 --num_epochs 25
+```
+
+## Inference
+- Model's semantic segmentation performance on the given dataset will be evaluated in three accuracy measures: global pixel-wise classification accuracy, 
+intersection over union, and mean accuracy.
+- vis_results is used to visualize the results on the test set
+- Example run command:
+python fusenet_test.py --dataroot ./datasets/nyu_class_10_db.h5 --load_checkpoint ./checkpoints/rgb_only/nyu/best_model.pth.tar --vis_results True
+```
+
+
+
+## Citing FuseNet
+Caner Hazirbas, Lingni Ma, Csaba Domokos and Daniel Cremers, _"FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture"_, in proceedings of the 13th Asian Conference on Computer Vision, 2016.
+
+    @inproceedings{fusenet2016accv,
+     author    = "C. Hazirbas and L. Ma and C. Domokos and D. Cremers",
+     title     = "FuseNet: incorporating depth into semantic segmentation via fusion-based CNN architecture",
+     booktitle = "Asian Conference on Computer Vision",
+     year      = "2016",
+     month     = "November",
+    }