Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# Self-Supervised Learning Framework
+
+This project implements a Self-Supervised Learning (SSL) framework using the CIFAR-10 dataset and a ResNet-18 backbone. The goal of the project is to train a model to learn robust image representations without relying on labeled data. This framework utilizes contrastive learning with data augmentations and a custom contrastive loss function.
+
+---
+
+## **Features**
+**Data Augmentation**:
+
+- Random cropping, flipping, color jitter, grayscale conversion, Gaussian blur, and normalization.
+  
+**Backbone Architecture**:
+  
+- ResNet-18 with a custom projection head.
+  
+**Contrastive Learning**:
+  
+- Contrastive loss function with positive and negative pair sampling.
+
+**Optimization**:
+  
+- Gradient clipping and weight decay for numerical stability.
+  
+**Model Checkpointing**:
+
+ - Save model weights at the end of each epoch.
+
+
+## **How It Works**
+1. **Data Augmentation**:
+   - Two augmented views of each image are created for contrastive learning.
+
+2. **Contrastive Loss**:
+   - Positive pairs: Augmented views of the same image.
+   - Negative pairs: Augmented views of different images.
+   - Loss is computed using the similarity of positive pairs while minimizing similarity with negative pairs.
+
+3. **Optimization**:
+   - The model uses the Adam optimizer with a learning rate of `3e-4` and weight decay of `1e-4`.
+   - Gradient clipping ensures numerical stability.
+
+---
+
+## **Results and Evaluation**
+- **Training Loss**:
+  - Observe the training loss decreasing across epochs, indicating successful representation learning.
+- **Downstream Tasks**:
+  - Evaluate the learned embeddings on classification or clustering tasks.
+
+---
+
+## **Acknowledgments**
+- CIFAR-10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
+- PyTorch: https://pytorch.org/
+- ResNet-18 architecture.
+
+---
+