https://github.com/kyegomez/simpleunet

An simple implementation of Unet because all the implementations i've seen are wayy tooo complicated.
https://github.com/kyegomez/simpleunet

artificial-intelligence biomedical biomedical-image-processing computer-vision gpt4 image image-classification image-segmentation texttovide unet vllm

Last synced: 2 months ago
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An simple implementation of Unet because all the implementations i've seen are wayy tooo complicated.

• Host: GitHub
• URL: https://github.com/kyegomez/simpleunet
• Owner: kyegomez
• License: mit
• Created: 2023-09-19T15:48:08.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-07T23:26:31.000Z (3 months ago)
• Last Synced: 2024-10-14T02:36:38.881Z (3 months ago)
• Topics: artificial-intelligence, biomedical, biomedical-image-processing, computer-vision, gpt4, image, image-classification, image-segmentation, texttovide, unet, vllm
• Language: Python
• Homepage: https://discord.gg/qUtxnK2NMf
• Size: 205 KB
• Stars: 7
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Unet

My implemenetation of a modular Unet from the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation"

[Paper Link](https://arxiv.org/abs/1505.04597)

# Appreciation

* Lucidrains

* Agorians

# Install

`pip install unet-torch`

# Usage

```python

import torch

from unet.model import Unet

model = Unet(n_channels=3, n_classes=1)

inputs = torch.randn(1, 3, 256, 256)

outputs = model(inputs)

print(outputs.size)

print(outputs)

```

# License

MIT

# Citations

```bibtex

@misc{1505.04597,

Author = {Olaf Ronneberger and Philipp Fischer and Thomas Brox},

Title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},

Year = {2015},

Eprint = {arXiv:1505.04597},

}

```