https://github.com/swarajkumarsingh/cnn-cifar-classification-model
Cifar classification model using Pytorch CNN module with ResNet9 model, with CUDA for training to archive 75% accuracy
https://github.com/swarajkumarsingh/cnn-cifar-classification-model
cifar10-classification cnn-classification cnn-model cnn-pytorch deep-learning python pytorch supervised-learning traing
Last synced: 3 months ago
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Cifar classification model using Pytorch CNN module with ResNet9 model, with CUDA for training to archive 75% accuracy
- Host: GitHub
- URL: https://github.com/swarajkumarsingh/cnn-cifar-classification-model
- Owner: swarajkumarsingh
- License: mit
- Created: 2024-08-05T13:14:43.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-09-18T08:53:20.000Z (10 months ago)
- Last Synced: 2024-09-18T11:42:59.390Z (10 months ago)
- Topics: cifar10-classification, cnn-classification, cnn-model, cnn-pytorch, deep-learning, python, pytorch, supervised-learning, traing
- Language: Jupyter Notebook
- Homepage: https://github.com/swarajkumarsingh/cnn-cifar-classification-model
- Size: 985 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## CNN Model for CIFAR-10 Classification
### Project Overview
This project involves training a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
### Model Architecture
The CNN model is designed with multiple convolutional layers, followed by max-pooling layers, and fully connected layers. The architecture is as follows:
1. Convolutional Layer 1: 32 filters, 3x3 kernel, ReLU activation
2. Max Pooling Layer 1: 2x2 pool size
3. Convolutional Layer 2: 64 filters, 3x3 kernel, ReLU activation
4. Max Pooling Layer 2: 2x2 pool size
5. Fully Connected Layer 1: 512 units, ReLU activation
6. Output Layer: 10 units, Softmax activation
```
class Cifar10CnnModel(ImageClassificationBase):
def __init__(self):
super().__init__()
self.network = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 64 x 16 x 16
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 128 x 8 x 8
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 256 x 4 x 4
nn.Flatten(),
nn.Linear(256*4*4, 1024), # activation layer 1
nn.ReLU(),
nn.Linear(1024, 512), # activation layer 2
nn.ReLU(),
nn.Linear(512, 10)) # activation layer 3
def forward(self, xb):
return self.network(xb)
```
### Benchmarks - 75% Accuracy
### CNN Kernel overview
### 2x2 Max-Pool diagram
### Conv2d
### Dataset
The CIFAR-10 dataset is used, which can be downloaded from the official website.
### Training
The model is trained using the following parameters:
- Optimizer: Adam
- Loss Function: Categorical Crossentropy
- Batch Size: 128
### Installation
1. Clone the repository:
```
git clone https://github.com/swarajkumarsingh/cnn-cifar-classification-model.git
```
2. Open Google Collab and open the model file
### Results
The trained model achieves an accuracy of approximately 75% on the test set.
### Contributing
Contributions are welcome! Please fork the repository and create a pull request with your changes.
### License
This project is licensed under the MIT License. See the LICENSE file for more details.
### Acknowledgments
- The CIFAR-10 dataset creators
- Pytorch and nn Module