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https://github.com/acecoooool/lednet
This is an unofficial implemention of LEDNet https://arxiv.org/abs/1905.02423
https://github.com/acecoooool/lednet
lightning-network pytorch segmentation
Last synced: 8 days ago
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This is an unofficial implemention of LEDNet https://arxiv.org/abs/1905.02423
- Host: GitHub
- URL: https://github.com/acecoooool/lednet
- Owner: AceCoooool
- License: mit
- Created: 2019-05-15T07:50:01.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-05-25T02:28:24.000Z (over 5 years ago)
- Last Synced: 2023-10-19T22:54:27.241Z (about 1 year ago)
- Topics: lightning-network, pytorch, segmentation
- Language: Python
- Homepage:
- Size: 12.7 MB
- Stars: 38
- Watchers: 5
- Forks: 10
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LEDNet
This is an unofficial implement of [LEDNet](https://arxiv.org/abs/1905.02423).
> the official version：[LEDNet-official](https://github.com/xiaoyufenfei/LEDNet)
## Environment
- Python 3.6
- PyTorch 1.1
## Performance
- Base Size 1024, Crop Size 768, only fine. (new-version, with dropout)
| Model | Paper | OHEM | Drop-rate | lr | Epoch | val (crop) | val |
| :----: | :---: | :--: | :-------: | :----: | :---: | :---------: | :----------------------------------------------------------: |
| LEDNet | / | ✗ | 0.1 | 0.0005 | 800 | 60.32/94.51 | 66.29/94.40 |
| LEDNet | / | ✗ | 0.1 | 0.005 | 600 | 61.29/94.75 | 66.56/94.72 |
| LEDNet | / | ✗ | 0.3 | 0.01 | 800 | 63.84/94.83 | [69.09/94.75](https://drive.google.com/open?id=1oelPUKAnZYD75RruyBQU9HZKneMEMIAp) |
> Note:
>
> - The paper only provide the test results: 69.2/86.8 (class mIoU/category mIoU).
> - And the training setting is a little different with original paper (original paper use 1024x512)
Some things you can use to improve the performance:
1. use larger learning rate (like 0.01)
2. use more epochs (like 1000)
3. use larger training input size (like Base Size 1344, Crop Size 1024)
## Demo
Please download [pretrained](https://drive.google.com/open?id=1oelPUKAnZYD75RruyBQU9HZKneMEMIAp) model first
```shell
$ python demo.py [--input-pic png/demo.png] [--pretrained your-root-of-pretrained] [--cuda true]
```
## Evaluation
The default data root is `~/.torch/datasets` (You can download dataset and build a soft-link to it)
```shell
$ python eval.py [--mode testval] [--pretrained root-of-pretrained-model] [--cuda true]
```
## Training
Recommend to using distributed training.
```shell
$ export NGPUS=4
$ python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py [--dataset citys] [--batch-size 8] [--base-size 1024] [--crop-size 768] [--epochs 800] [--warmup-factor 0.1] [--warmup-iters 200] [--log-step 10] [--save-epoch 40] [--lr 0.005]
```
## Prepare data
Your can reference [gluon-cv-cityspaces](https://gluon-cv.mxnet.io/build/examples_datasets/cityscapes.html#sphx-glr-build-examples-datasets-cityscapes-py) to prepare the dataset