Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- video-classification
+- timesformer
+- retnet
+- action-recognition
+- hmdb51
+- efficient-models
+- transformers
+datasets:
+- hmdb51
+---
+
+# 🎬 RetFormer: Efficient TimeSformer + RetNet for Video Action Recognition
+
+RetFormer is a hybrid video classification model that replaces the **temporal attention** in TimeSformer with **RetNet**, achieving:
+
+- ⚡ Lower memory usage  
+- 🚀 Faster training  
+- 🎯 Competitive accuracy  
+
+---
+
+## 🧠 Model Architecture
+
+### 🔹 RetFormer (Proposed)
+- Spatial Modeling → TimeSformer  
+- Temporal Modeling → **RetNet**  
+
+👉 This replaces quadratic attention with **linear-time temporal modeling (O(n))**
+
+---
+
+## 📊 Dataset
+
+- **HMDB51**
+  - 51 human action classes
+  - Complex motion patterns
+  - Smaller and more challenging than UCF101
+
+---
+
+## 🔁 Training Strategy
+
+Training was performed in multiple stages due to runtime limits:
+
+- Initial training (Epoch 1–10)
+- Checkpoint saving
+- Resumed training (Epoch 11–14)
+- Early stopping applied
+
+---
+
+## 📈 Training Results (Epoch 1–14)
+
+| 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 | 1.7325 | 0.7524 | 1.7315 | **0.6699** | **0.6614** |
+| 12 | 1.7036 | 0.7584 | 1.7469 | 0.6621 | 0.6515 |
+| 13 | 1.6682 | 0.7717 | 1.7504 | 0.6595 | 0.6496 |
+| 14 | 1.6344 | 0.7785 | 1.7488 | 0.6588 | 0.6494 |
+
+---
+
+## 🏆 Best Performance
+
+- **Validation Accuracy:** **66.99%**
+- **F1 Score:** 0.6614  
+- Achieved at **Epoch 11**
+
+---
+
+## ⚙️ Training Details
+
+- Peak GPU Memory: **~7.2 GB**
+- Training Time per Epoch: ~52 minutes
+- Evaluation Time: ~8 minutes
+- Mixed Precision Training (`torch.cuda.amp`)
+- Early stopping triggered after Epoch 14
+
+---
+
+## 📌 Observations
+
+- Stable improvement until **Epoch 11**
+- Slight decline afterward → early overfitting
+- Lower accuracy than baseline (expected for hybrid trade-off)
+
+---
+
+## ⚡ Efficiency Advantage
+
+| Metric | TimeSformer | RetFormer |
+|-------|------------|----------|
+| Peak GPU Memory | ~9.3 GB | **~7.2 GB** ✅ |
+| Complexity | O(n²) | **O(n)** ✅ |
+| Speed | Slower | Faster |
+
+👉 **~25% reduction in GPU memory**
+
+---
+
+## 🔍 Key Insight
+
+RetFormer demonstrates that:
+
+- Efficient temporal modeling can **significantly reduce memory usage**
+- Performance remains **competitive with baseline models**
+- Trade-off exists between **efficiency and maximum accuracy**
+
+---
+
+## 🚀 Usage
+
+```bash
+pip install torch torchvision transformers