https://github.com/adeelh/pytorch-fpn

PyTorch implementations of some FPN-based semantic segmentation architectures: vanilla FPN, Panoptic FPN, PANet FPN; with ResNet and EfficientNet backbones.
https://github.com/adeelh/pytorch-fpn

computer-vision deep-learning deeplearning efficientnet feature-pyramid-network fpn implementation-of-research-paper machine-learning neural-network pytorch pytorch-fpn pytorch-implementation resnet semantic-segmentation semantic-segmentation-architectures

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PyTorch implementations of some FPN-based semantic segmentation architectures: vanilla FPN, Panoptic FPN, PANet FPN; with ResNet and EfficientNet backbones.

• Host: GitHub
• URL: https://github.com/adeelh/pytorch-fpn
• Owner: AdeelH
• License: mit
• Created: 2020-09-30T08:01:22.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2023-06-30T16:29:41.000Z (over 1 year ago)
• Last Synced: 2023-12-16T11:05:17.450Z (11 months ago)
• Topics: computer-vision, deep-learning, deeplearning, efficientnet, feature-pyramid-network, fpn, implementation-of-research-paper, machine-learning, neural-network, pytorch, pytorch-fpn, pytorch-implementation, resnet, semantic-segmentation, semantic-segmentation-architectures
• Language: Python
• Homepage:
• Size: 57.6 KB
• Stars: 46
• Watchers: 4
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pytorch-fpn

PyTorch implementation of the following semantic segmentation architectures (see `fpn.py`):

- **FPN**, *Feature Pyramid Networks for Object Detection* by Lin et al., https://arxiv.com/abs/1612.03144.

- **Panoptic FPN**, *Panoptic Feature Pyramid Networks* by Kirilov et al., https://arxiv.com/abs/1901.02446.

- **PANet FPN**, *Path Aggregation Network for Instance Segmentation* by Liu et al., https://arxiv.com/abs/1803.01534

The implementations are all based on `nn.Sequential` with no fancy forward methods, meaning that they can be easily modified and combined together or with other modules.

# Backbones

The repo provides factory functions (`make_fpn_resnet()` and `make_fpn_efficientnet()`) for creating FPN's with 2 kinds of backbones:

- ResNet backbones (via Torch Vision)

- EfficientNet backbones (via https://github.com/lukemelas/EfficientNet-PyTorch)

# Multiband images

The factory methods `make_fpn_resnet()` and `make_fpn_efficientnet()` support `in_channels != 3`.

`make_fpn_resnet()`, in particular, makes use of the fusion technique described in the paper, *FuseNet*, by Hazirbas et al. (https://vision.in.tum.de/_media/spezial/bib/hazirbasma2016fusenet.pdf) if `in_channels > 3` that adds a parallel resnet backbone for the new channels. All the pretrained weights are retained.

# Usage

```python

from factory import make_fpn_resnet

model = make_fpn_resnet(

	name='resnet18',

	fpn_type='fpn',

	pretrained=True,

	num_classes=2,

	fpn_channels=256,

	in_channels=3,

	out_size=(224, 224))

```

# Loading through torch.hub

This repo supports importing modules through `torch.hub`. The models can be easily imported into your code via the factory functions in `factory.py`.

```python

import torch

model = torch.hub.load(

	'AdeelH/pytorch-fpn',

	'make_fpn_resnet',

	name='resnet18',

	fpn_type='panoptic',

	num_classes=2,

	fpn_channels=256,

	in_channels=3,

	out_size=(224, 224)

)

```

Working example on Colab: https://colab.research.google.com/drive/1pjkw-QoqiXgKDrEYZ47EwfXcW7vgO4KX?usp=sharing