Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+## 📊 Baseline Model Results (TimeSformer on HMDB51)
+
+The standard **TimeSformer model** was trained on the **HMDB51 dataset** for 15 epochs.
+
+Training was performed in multiple stages due to runtime limits and resumed using saved checkpoints.
+
+---
+
+## 🔁 Training Strategy
+
+- Training conducted on Kaggle GPU
+- Interrupted due to 12-hour session limit
+- Resumed using `.safetensors` checkpoint
+- Completed full **15 epochs**
+- Early stopping applied at final stage
+
+---
+
+## 📈 Training Results (Epoch 1–15)
+
+| Epoch | Train Loss | Train Acc | Val Loss | Val Acc | F1 |
+|------|------------|-----------|----------|---------|-----|
+| 1 | 3.9314 | 0.0323 | 3.7800 | 0.1248 | 0.1100 |
+| 2 | 3.5892 | 0.1976 | 3.2530 | 0.4353 | 0.3959 |
+| 3 | 3.0235 | 0.4354 | 2.6567 | 0.5340 | 0.4867 |
+| 4 | 2.5720 | 0.5246 | 2.2347 | 0.6033 | 0.5720 |
+| 5 | 2.2836 | 0.6009 | 2.0135 | 0.6327 | 0.6143 |
+| 6 | 2.0897 | 0.6419 | 1.8659 | 0.6536 | 0.6415 |
+| 7 | 1.9678 | 0.6807 | 1.8017 | 0.6667 | 0.6561 |
+| 8 | 1.8847 | 0.7030 | 1.7705 | 0.6627 | 0.6485 |
+| 9 | 1.8213 | 0.7237 | 1.7355 | 0.6627 | 0.6518 |
+| 10 | 1.7576 | 0.7414 | 1.7340 | 0.6680 | 0.6570 |
+| 11 | 1.7098 | 0.7549 | 1.7234 | 0.6765 | 0.6699 |
+| 12 | 1.6813 | 0.7660 | 1.6980 | **0.6895** | **0.6846** |
+| 13 | 1.6731 | 0.7693 | 1.7023 | 0.6882 | 0.6824 |
+| 14 | 1.6384 | 0.7778 | 1.7110 | 0.6850 | 0.6806 |
+| 15 | 1.6045 | 0.7884 | 1.7131 | 0.6850 | 0.6805 |
+
+---
+
+## 🏆 Best Performance
+
+- **Validation Accuracy:** **68.95%**
+- **F1 Score:** 0.6846  
+- Achieved at **Epoch 12**
+
+---
+
+## ⚙️ Training Details
+
+- Peak GPU Memory: **~9.3 GB**
+- Training Time per Epoch: ~55 minutes
+- Evaluation Time: ~8 minutes
+- Mixed Precision Training used
+- Early stopping applied after convergence
+
+---
+
+## 📌 Observations
+
+- Strong and stable learning curve
+- Performance improves steadily until **Epoch 12**
+- After Epoch 12:
+  - Validation accuracy plateaus
+  - Validation loss increases slightly → **overfitting begins**
+
+---
+
+## ⚡ Key Insight
+
+- TimeSformer achieves **higher accuracy (~68.95%)** on HMDB51  
+- However, it requires:
+  - ❌ Higher memory (~9.3 GB)
+  - ❌ Higher computational cost  
+
+---
+
+## 🔍 Conclusion (Baseline)
+
+The TimeSformer model delivers strong performance on HMDB51 but at a significantly higher computational cost, highlighting the need for more efficient architectures such as the proposed RetNet-based hybrid model.