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README.md

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 README.md for tiny-cnn-classifier
 
-\# Tiny CNN Classifier for Image Classification (CIFAR-10)
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+Tiny CNN Classifier for Image Classification (CIFAR-10)
 
 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.
 
+Model Overview
 
+Type: Convolutional Neural Network (CNN)
 
-\## Model Overview
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-\- Type: Convolutional Neural Network (CNN)
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-\- Architecture:
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+Architecture:
   - 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
 
-\- 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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+Dataset: CIFAR-10 (10 classes)
 
+Test Accuracy: 69.90%
 
+How the Model Works:
 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
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+How to Use the Model:
 
 To use this model for image classification, you can use the following code snippet:
 
@@ -60,95 +41,49 @@ from transformers import pipeline
 
 
 
-\# Load the trained model
+# Load the trained model
 
 classifier = pipeline('image-classification', model='Udayan012/tiny-cnn-classifier')
 
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-\# Provide an image to classify
+# Provide an image to classify
 
 image = 'path\_to\_your\_image.jpg'
 
-
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-\# Get the classification result
+# Get the classification result
 
 result = classifier(image)
 
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-\# Print the result
+# Print the result
 
 print(result)
 
+Steps to classify your own images:-
 
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-Steps to classify your own images:
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-Install necessary libraries:
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+1. Install necessary libraries:
 
 pip install transformers torch torchvision
 
+2. Use the pipeline() function to load the model and classify images.
 
 
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-Use the pipeline() function to load the model and classify images.
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-Training Information
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+Training Information:
 
 Dataset: CIFAR-10
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 Optimizer: Adam
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 Loss Function: Cross-Entropy Loss
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 Training Epochs: 5
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 Batch Size: 32
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 Learning Rate: 0.001
 
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-Model Limitations
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+Model Limitations:
 
 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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 It performs best on 32x32 pixel images with simple backgrounds, similar to those in the CIFAR-10 dataset.
 
 
 
-License
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+License:
 
 This model is released under the Apache 2.0 License. You can freely use, modify, and distribute this model.