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Semantic Segmentation Architectures Implemented in PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 3407 |
| 2026-07-25 | 3407 |
| 2026-07-28 | 3407 |
| 2026-07-30 | 3407 |
| 2026-08-06 | 3407 |
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# 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.
<p align="center">
<a href="https://www.youtube.com/watch?v=iXh9aCK3ubs" target="_blank"><img src="https://i.imgur.com/agvJOPF.gif" width="364"/></a>
<img src="https://meetshah1995.github.io/images/blog/ss/ptsemseg.png" width="49%"/>
</p>
### 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: <name> [options: 'fcn[8,16,32]s, unet, segnet, pspnet, icnet, icnetBN, linknet, frrn[A,B]'
<model_keyarg_1>:<value>
# Data Configuration
data:
dataset: <name> [options: 'pascal, camvid, ade20k, mit_sceneparsing_benchmark, cityscapes, nyuv2, sunrgbd, vistas']
train_split: <split_to_train_on>
val_split: <spit_to_validate_on>
img_rows: 512
img_cols: 1024
path: <path/to/data>
<dataset_keyarg1>:<value>
# Training Configuration
training:
n_workers: 64
train_iters: 35000
batch_size: 16
val_interval: 500
print_interval: 25
loss:
name: <loss_type> [options: 'cross_entropy, bootstrapped_cross_entropy, multi_scale_crossentropy']
<loss_keyarg1>:<value>
# Optmizer Configuration
optimizer:
name: <optimizer_name> [options: 'sgd, adam, adamax, asgd, adadelta, adagrad, rmsprop']
lr: 1.0e-3
<optimizer_keyarg1>:<value>
# Warmup LR Configuration
warmup_iters: <iters for lr warmup>
mode: <'constant' or 'linear' for warmup'>
gamma: <gamma for warm up>
# 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]
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:109cdec8873b7c31, topic:deep-learning, topic:pytorch