https://github.com/zfturbo/timm_3d

PyTorch Volume Models for 3D data
https://github.com/zfturbo/timm_3d

3d-classification 3d-convolutional-network 3d-maxvit 3d-models 3d-resnet 3d-vgg classification densenet-pytorch efficientnet maxvit pretrained-models pytorch timm vgg

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PyTorch Volume Models for 3D data

• Host: GitHub
• URL: https://github.com/zfturbo/timm_3d
• Owner: ZFTurbo
• Created: 2024-03-04T08:09:52.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-08-01T17:15:16.000Z (4 months ago)
• Last Synced: 2024-11-14T10:06:33.034Z (about 24 hours ago)
• Topics: 3d-classification, 3d-convolutional-network, 3d-maxvit, 3d-models, 3d-resnet, 3d-vgg, classification, densenet-pytorch, efficientnet, maxvit, pretrained-models, pytorch, timm, vgg
• Language: Python
• Homepage:
• Size: 1.72 MB
• Stars: 40
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# PyTorch Volumes Models for 3D data 

Python library with Neural Networks for Volume (3D) Classification based on PyTorch.

This library is based on famous [PyTorch Image Models (timm)](https://github.com/huggingface/pytorch-image-models) library for images. Most of the documentation can be used directly from there. 

## Installation

* Type 1: `pip install timm-3d`

* Type 2: Copy `timm_3d` folder from this repository in your project folder.

## Quick start

You can create model as easy as:

```python

import timm_3d

import torch

m = timm_3d.create_model(

    'tf_efficientnet_b0.in1k',

    pretrained=True,

    num_classes=0,

    global_pool=''

)

# Shape of input (B, C, H, W, D). B - batch size, C - channels, H - height, W - width, D - depth

res = m(torch.randn(2, 3, 128, 128, 128))

print(f'Output shape: {res.shape}') 

```

* **Note 1**: you can use pretrained weights. They will be converted on the fly from 2D to 3D variant.

* **Note 2**: More examples can be found [here](test.py) 

## Models

Currently supported models in 3D variant

* Efficientnet family [[Code](timm_3d/models/efficientnet.py)]

* ResNet family [[Code](timm_3d/models/resnet.py)]

* CoAtNet and MaxVit family [[Code](timm_3d/models/maxxvit.py)]

* ConvNext family [[Code](timm_3d/models/convnext.py)]

* DenseNet family [[Code](timm_3d/models/densenet.py)]

* VGG family [[Code](timm_3d/models/vgg.py)]

[Full list of all possible models](docs/models_list.md)

## Notes for 3D version

### Input size

Recommended input size for backbones can be calculated as: `K = pow(N, 2/3)`. 

Where N - is size for input image for the same model in 2D variant.

For example for N = 224, K = 32. For N = 512, K = 64.

### Related repositories

 * [pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models) - original 2D repo

 * [classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) - 3D volumes classification models for keras/tensorflow

 * [segmentation_models_pytorch_3d](https://github.com/ZFTurbo/segmentation_models_pytorch_3d) - 3D volumes segmentation models for PyTorch

 * [volumentations](https://github.com/ZFTurbo/volumentations) - 3D augmentations

## Citation

If you find this code useful, please cite it as:

```

@article{solovyev20223d,

  title={3D convolutional neural networks for stalled brain capillary detection},

  author={Solovyev, Roman and Kalinin, Alexandr A and Gabruseva, Tatiana},

  journal={Computers in Biology and Medicine},

  volume={141},

  pages={105089},

  year={2022},

  publisher={Elsevier},

  doi={10.1016/j.compbiomed.2021.105089}

}

```

## To Do List

* Add support for more architectures