Update README.md

README.md

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-license: apache-2.0
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+## 📊 Hybrid Model Results (HMDB51)
+
+The hybrid model (TimeSformer + RetNet) was also trained on the **HMDB51 dataset**.
+
+Due to Kaggle’s runtime limitation, training was interrupted at **Epoch 12**, so results are reported up to **Epoch 11**. Training will be resumed in a later stage.
+
+---
+
+### 🔹 Training Results (Epoch 1–11)
+
+| Epoch | Train Loss | Train Acc | Val Loss | Val Acc | F1 |
+|------|------------|-----------|----------|---------|-----|
+| 1 | 3.9312 | 0.0350 | 3.8099 | 0.0967 | 0.0855 |
+| 2 | 3.6330 | 0.1791 | 3.2948 | 0.3654 | 0.3149 |
+| 3 | 3.0989 | 0.3691 | 2.6927 | 0.5150 | 0.4579 |
+| 4 | 2.6278 | 0.5048 | 2.2879 | 0.5869 | 0.5503 |
+| 5 | 2.3198 | 0.5782 | 2.0438 | 0.6255 | 0.5961 |
+| 6 | 2.1387 | 0.6194 | 1.9152 | 0.6242 | 0.6074 |
+| 7 | 1.9876 | 0.6657 | 1.8369 | 0.6418 | 0.6308 |
+| 8 | 1.9140 | 0.6936 | 1.7966 | 0.6359 | 0.6188 |
+| 9 | 1.8539 | 0.7041 | 1.7619 | 0.6556 | 0.6426 |
+| 10 | 1.8149 | 0.7244 | 1.7523 | **0.6614** | **0.6512** |
+| 11 | 1.7270 | 0.7561 | 1.7543 | 0.6556 | 0.6472 |
+
+---
+
+## 🏆 Best Performance (Current)
+
+- **Validation Accuracy:** **66.14%**
+- **F1 Score:** 0.6512  
+- Achieved at **Epoch 10**
+
+---
+
+## ⚠️ Training Status
+
+- Training **interrupted at Epoch 12** due to runtime limit
+- Model will be **resumed from best checkpoint**
+- Final performance may improve after full training
+
+---
+
+## ⚡ Efficiency
+
+- Peak GPU Memory: **~7.2 GB**
+- ~25% lower than standard TimeSformer
+- Faster training per epoch
+
+---
+
+## 📌 Observations
+
+- Steady improvement until Epoch 10  
+- Slight plateau after that (possible early convergence)  
+- Lower accuracy compared to UCF101 (expected due to dataset complexity)
+
+---
+
+## 🔄 Next Steps
+
+- Resume training from Epoch 11 checkpoint  
+- Complete remaining epochs  
+- Compare final performance with baseline model