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---
tags:
- custom
- cifar-10
- image-classification
- block-architecture
language: en
framework: pytorch
metrics:
- accuracy: 75.43
license_name: mit
datasets:
- CIFAR-10
---
# BlockNet10 - CNN for CIFAR-10 dataset
## Overview
BlockNet10 is a neural network architecture designed for image classification tasks using the CIFAR-10 dataset. This model implements a sequence of intermediate blocks (B1, B2, ..., BK) followed by an output block (O).
## Architecture Details
### Intermediate Block (Bi)
Each intermediate block receives an input image x and outputs an image x'. The block comprises L independent convolutional layers, denoted as C1, C2, ..., CL.
Each convolutional layer Cl in a block operates on the input image x and outputs an image Cl(x).
<div style="display: flex; justify-content: center;">
<img src="figures/eq1.png" alt="Equation 1" />
</div>
The output image x' is computed as x' = a1C1(x) + a2C2(x) + ... + aLCL(x), where a = [a1, a2, ..., aL]T is a vector computed by the block.
The vector a is obtained by computing the average value of each channel of x and passing it through a fully connected layer with the same number of units as convolutional layers in the block.
<div style="display: flex; justify-content: center;">
<img src="figures/fig1.png" alt="Figure 1" />
</div>
### Output Block (O)
The output block processes the final output image from the intermediate blocks for classification.
## Analytics
<div style="display: flex; justify-content: center; align-items: center;">
<table>
<tr>
<th>Epoch Number</th>
<th>Train Accuracy</th>
<th>Test Accuracy</th>
<th>Average Loss</th>
</tr>
<tr>
<td>50</td>
<td>75.43</td>
<td>80.56</td>
<td>0.685</td>
</tr>
</table>
</div>
## Clone on GitHub
You can contribute to the advancement of this architecture, changes in hyperparameter, or solve issues <a href="https://github.com/siddheshtv/cifar10" target="_blank">here</a>.
## Citation
If you use BlockNet10 in your research or work, please cite it as follows:
```bibtex
@article{blocknet10,
title={BlockNet10: CIFAR-10 Image Classifier},
author={Siddhesh Kulthe},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/siddheshtv/BlockNet10}
}
```
---
## license: mit
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