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https://github.com/meetps/pytorch-semseg
Semantic Segmentation Architectures Implemented in PyTorch
https://github.com/meetps/pytorch-semseg
deep-learning fully-convolutional-networks pytorch semantic-segmentation
Last synced: 5 days ago
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Semantic Segmentation Architectures Implemented in PyTorch
- Host: GitHub
- URL: https://github.com/meetps/pytorch-semseg
- Owner: meetps
- License: mit
- Created: 2017-03-22T20:28:59.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-10-11T05:27:04.000Z (over 1 year ago)
- Last Synced: 2025-01-09T20:19:50.489Z (12 days ago)
- Topics: deep-learning, fully-convolutional-networks, pytorch, semantic-segmentation
- Language: Python
- Homepage: https://meetshah.dev/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html
- Size: 272 KB
- Stars: 3,402
- Watchers: 106
- Forks: 792
- Open Issues: 133
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pytorch-semseg
[](https://github.com/meetshah1995/pytorch-semseg/blob/master/LICENSE)
[](https://pypi.python.org/pypi/pytorch-semseg/0.1.2)
[](https://doi.org/10.5281/zenodo.1185075)
## Semantic Segmentation Algorithms Implemented in PyTorch
This repository aims at mirroring popular semantic segmentation architectures in PyTorch.
### Networks implemented
* [PSPNet](https://arxiv.org/abs/1612.01105) - With support for loading pretrained models w/o caffe dependency
* [ICNet](https://arxiv.org/pdf/1704.08545.pdf) - With optional batchnorm and pretrained models
* [FRRN](https://arxiv.org/abs/1611.08323) - Model A and B
* [FCN](https://arxiv.org/abs/1411.4038) - All 1 (FCN32s), 2 (FCN16s) and 3 (FCN8s) stream variants
* [U-Net](https://arxiv.org/abs/1505.04597) - With optional deconvolution and batchnorm
* [Link-Net](https://codeac29.github.io/projects/linknet/) - With multiple resnet backends
* [Segnet](https://arxiv.org/abs/1511.00561) - With Unpooling using Maxpool indices
#### Upcoming
* [E-Net](https://arxiv.org/abs/1606.02147)
* [RefineNet](https://arxiv.org/abs/1611.06612)
### DataLoaders implemented
* [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
* [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/segexamples/index.html)
* [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/)
* [MIT Scene Parsing Benchmark](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip)
* [Cityscapes](https://www.cityscapes-dataset.com/)
* [NYUDv2](http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
* [Sun-RGBD](http://rgbd.cs.princeton.edu/)
### Requirements
* pytorch >=0.4.0
* torchvision ==0.2.0
* scipy
* tqdm
* tensorboardX
#### One-line installation
`pip install -r requirements.txt`
### Data
* Download data for desired dataset(s) from list of URLs [here](https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html#sec_datasets).
* Extract the zip / tar and modify the path appropriately in your `config.yaml`
### Usage
**Setup config file**
```yaml
# Model Configuration
model:
arch: [options: 'fcn[8,16,32]s, unet, segnet, pspnet, icnet, icnetBN, linknet, frrn[A,B]'
:
# Data Configuration
data:
dataset: [options: 'pascal, camvid, ade20k, mit_sceneparsing_benchmark, cityscapes, nyuv2, sunrgbd, vistas']
train_split:
val_split:
img_rows: 512
img_cols: 1024
path:
:
# Training Configuration
training:
n_workers: 64
train_iters: 35000
batch_size: 16
val_interval: 500
print_interval: 25
loss:
name: [options: 'cross_entropy, bootstrapped_cross_entropy, multi_scale_crossentropy']
:
# Optmizer Configuration
optimizer:
name: [options: 'sgd, adam, adamax, asgd, adadelta, adagrad, rmsprop']
lr: 1.0e-3
:
# Warmup LR Configuration
warmup_iters:
mode: <'constant' or 'linear' for warmup'>
gamma:
# Augmentations Configuration
augmentations:
gamma: x #[gamma varied in 1 to 1+x]
hue: x #[hue varied in -x to x]
brightness: x #[brightness varied in 1-x to 1+x]
saturation: x #[saturation varied in 1-x to 1+x]
contrast: x #[contrast varied in 1-x to 1+x]
rcrop: [h, w] #[crop of size (h,w)]
translate: [dh, dw] #[reflective translation by (dh, dw)]
rotate: d #[rotate -d to d degrees]
scale: [h,w] #[scale to size (h,w)]
ccrop: [h,w] #[center crop of (h,w)]
hflip: p #[flip horizontally with chance p]
vflip: p #[flip vertically with chance p]
# LR Schedule Configuration
lr_schedule:
name: [options: 'constant_lr, poly_lr, multi_step, cosine_annealing, exp_lr']
:
# Resume from checkpoint
resume:
```
**To train the model :**
```
python train.py [-h] [--config [CONFIG]]
--config Configuration file to use
```
**To validate the model :**
```
usage: validate.py [-h] [--config [CONFIG]] [--model_path [MODEL_PATH]]
[--eval_flip] [--measure_time]
--config Config file to be used
--model_path Path to the saved model
--eval_flip Enable evaluation with flipped image | True by default
--measure_time Enable evaluation with time (fps) measurement | True
by default
```
**To test the model w.r.t. a dataset on custom images(s):**
```
python test.py [-h] [--model_path [MODEL_PATH]] [--dataset [DATASET]]
[--dcrf [DCRF]] [--img_path [IMG_PATH]] [--out_path [OUT_PATH]]
--model_path Path to the saved model
--dataset Dataset to use ['pascal, camvid, ade20k etc']
--dcrf Enable DenseCRF based post-processing
--img_path Path of the input image
--out_path Path of the output segmap
```
**If you find this code useful in your research, please consider citing:**
```
@article{mshahsemseg,
Author = {Meet P Shah},
Title = {Semantic Segmentation Architectures Implemented in PyTorch.},
Journal = {https://github.com/meetshah1995/pytorch-semseg},
Year = {2017}
}
```