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--- |
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language: en |
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license: mit |
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tags: |
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- image-classification |
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- computer-vision |
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- pytorch |
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- cifar10 |
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datasets: |
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- cifar10 |
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metrics: |
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- accuracy |
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--- |
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# CIFAR-10 CNN Model |
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This is a convolutional neural network trained on the CIFAR-10 dataset, achieving 92.59% test accuracy after 100 epochs. |
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## Model Details |
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- **Architecture**: 9 convolutional layers with batch normalization, max pooling, and dropout, followed by 3 fully connected layers. |
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- **Dataset**: CIFAR-10 (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck). |
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- **Training**: 100 epochs, SGD optimizer, CrossEntropyLoss, learning rate scheduling. |
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- **Accuracy**: 92.59% on the CIFAR-10 test set. |
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## Usage |
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Load the model using: |
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```python |
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from huggingface_hub import from_pretrained_pytorch |
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model = from_pretrained_pytorch('chandu1617/CIFAR10-CNN_Model') |
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``` |
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## Interactive Demo |
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Try the model in an interactive Gradio UI at [chandu1617/cifar10-cnn-demo](https://huggingface.co/spaces/chandu1617/cifar10-cnn-demo). |
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## Training Details |
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- **Optimizer**: SGD with momentum 0.9, weight decay 1e-6. |
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- **Learning Rate**: Initial 0.01, reduced on plateau (factor 0.1, patience 10, min_lr 0.00001). |
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- **Data Augmentation**: Color jitter, random perspective, random horizontal flip, normalization. |
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