https://github.com/debugger404/multi-dimensional-image-modeling

PyTorch Multi-Dimension Model Training - 🖼️ Training models on random multi-dimensional images with labels. Includes datasets of 8D images and supports image classification using pretrained models like ResNet18, VGG16, DenseNet161, and AlexNet. Adjustable for 3D image training.
https://github.com/debugger404/multi-dimensional-image-modeling

image image-processing model multidimensional python pytorch traninig

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PyTorch Multi-Dimension Model Training - 🖼️ Training models on random multi-dimensional images with labels. Includes datasets of 8D images and supports image classification using pretrained models like ResNet18, VGG16, DenseNet161, and AlexNet. Adjustable for 3D image training.

• Host: GitHub
• URL: https://github.com/debugger404/multi-dimensional-image-modeling
• Owner: deBUGger404
• Created: 2021-05-08T12:55:19.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-05-08T15:23:48.000Z (over 3 years ago)
• Last Synced: 2024-11-05T09:48:35.677Z (about 2 months ago)
• Topics: image, image-processing, model, multidimensional, python, pytorch, traninig
• Language: Python
• Homepage:
• Size: 43.9 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Multi-Dimension Model Training in PyTorch



 



   

## Dataset

In this project, model build for multi dimension images like image dimension >3(rgb). so data created in such a manner where images are random 8 dimension images with their respective random labels.

```

   self.image = np.random.rand(5000,224,224,8)

   self.labels = np.random.choice([0, 1], size=(5000,), p=[0.6,0.4])

```

## Image classification using pretrained model on random multi dimension image data

Below are the prettrained model used for this problem:

1. resnet18

2. vgg16

3. densenet161

4. alexnet

>If train the model for 3-dimensional image then change input_dim = 3 

## prediction

```python

import torch

from utils.utils import *

x,y = dataset

model = torch.load('model_multi_dim.pth')

y_pred = model(x)

accuracy = binary_acc(y_pred,y)

```

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