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README.md

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-license: apache-2.0
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+README.md for tiny-cnn-classifier
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+\# Tiny CNN Classifier for Image Classification (CIFAR-10)
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+
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+This is a custom Convolutional Neural Network (CNN) model trained on the CIFAR-10 dataset. The model classifies images into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. It was trained using the PyTorch framework.
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+
+
+\## Model Overview
+
+
+
+\- Type: Convolutional Neural Network (CNN)
+
+\- Architecture:
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+  - 2 convolutional layers
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+  - 2 max-pooling layers
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+  - 2 fully connected layers
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+  - ReLU activation functions
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+\- Dataset: CIFAR-10 (10 classes)
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+\- Test Accuracy: 69.90%
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+
+
+\## How the Model Works
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+
+
+The model uses two convolutional layers followed by max-pooling and fully connected layers to classify images. The model was trained for 5 epochs on the CIFAR-10 dataset.
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+
+
+\## How to Use the Model
+
+
+
+To use this model for image classification, you can use the following code snippet:
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+
+
+```python
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+from transformers import pipeline
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+
+
+\# Load the trained model
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+classifier = pipeline('image-classification', model='Udayan012/tiny-cnn-classifier')
+
+
+
+\# Provide an image to classify
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+image = 'path\_to\_your\_image.jpg'
+
+
+
+\# Get the classification result
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+result = classifier(image)
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+
+
+\# Print the result
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+print(result)
+
+
+
+Steps to classify your own images:
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+
+
+Install necessary libraries:
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+
+
+pip install transformers torch torchvision
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+
+
+
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+Use the pipeline() function to load the model and classify images.
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+
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+Training Information
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+
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+Dataset: CIFAR-10
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+
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+Optimizer: Adam
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+
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+Loss Function: Cross-Entropy Loss
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+
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+Training Epochs: 5
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+
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+Batch Size: 32
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+
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+Learning Rate: 0.001
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+Model Limitations
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+
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+The model is trained on the CIFAR-10 dataset and performs well on images similar to the CIFAR-10 test set.
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+The model may not generalize well to high-resolution images or images with complex backgrounds.
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+
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+It performs best on 32x32 pixel images with simple backgrounds, similar to those in the CIFAR-10 dataset.
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+
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+License
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+
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+This model is released under the Apache 2.0 License. You can freely use, modify, and distribute this model.
+