pochih / Fcn Pytorch
🚘 Easiest Fully Convolutional Networks
Stars: ✭ 278
Programming Languages
python
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🚘 The easiest implementation of fully convolutional networks
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Task: semantic segmentation, it's a very important task for automated driving
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The model is based on CVPR '15 best paper honorable mentioned Fully Convolutional Networks for Semantic Segmentation
Results
Trials
Training Procedures
Performance
I train with two popular benchmark dataset: CamVid and Cityscapes
| dataset | n_class | pixel accuracy |
|---|---|---|
| Cityscapes | 20 | 96% |
| CamVid | 32 | 93% |
Training
Install packages
pip3 install -r requirements.txt
and download pytorch 0.2.0 from pytorch.org
and download CamVid dataset (recommended) or Cityscapes dataset
Run the code
- default dataset is CamVid
create a directory named "CamVid", and put data into it, then run python codes:
python3 python/CamVid_utils.py
python3 python/train.py CamVid
- or train with CityScapes
create a directory named "CityScapes", and put data into it, then run python codes:
python3 python/CityScapes_utils.py
python3 python/train.py CityScapes
Author
Po-Chih Huang / @pochih
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