Create README.md

README.md

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+---
+license: mit
+tags:
+- video-classification
+- timesformer
+- action-recognition
+- ucf101
+- pytorch
+datasets:
+- ucf101
+---
+
+# 🎬 TimeSformer Fine-Tuned for Video Action Recognition
+
+This model is a fine-tuned version of **TimeSformer (Time-Space Transformer)** for **video action recognition**, trained on the **UCF101 dataset**.
+
+---
+
+## 📌 Model Overview
+
+- **Base Model:** facebook/timesformer-base-finetuned-k400  
+- **Task:** Video Classification / Action Recognition  
+- **Dataset:** UCF101 (101 action classes)  
+- **Framework:** PyTorch + Hugging Face Transformers  
+- **Training Environment:** Kaggle (GPU)
+
+---
+
+## 🧠 Training Strategy
+
+Due to Kaggle’s **12-hour session limit**, training was performed in **multiple stages**:
+
+1. Initial training run
+2. Checkpoint saving (best model)
+3. Resume training from best checkpoint
+4. Further fine-tuning across sessions
+
+This approach ensures efficient long training without losing progress.
+
+---
+
+## 📊 Training Results
+
+### 🔹 Initial Training
+
+| Epoch | Train Loss | Train Acc | Val Loss | Val Acc |
+|------|------------|-----------|----------|---------|
+| 1 | 4.5066 | 0.0622 | 4.1089 | 0.4245 |
+| 2 | 3.5721 | 0.4711 | 2.5276 | 0.8007 |
+| 3 | 2.3239 | 0.7323 | 1.4321 | 0.8993 |
+
+---
+
+### 🔹 Continued Training (Checkpoint Resume)
+
+| Epoch | Train Loss | Train Acc | Val Loss | Val Acc |
+|------|------------|-----------|----------|---------|
+| 4 | 1.8289 | 0.7991 | 1.1802 | 0.9199 |
+| 5 | 1.7119 | 0.8094 | 1.1372 | 0.9128 |
+| 6 | 1.6365 | 0.8153 | 1.1085 | 0.9191 |
+| 7 | 1.5982 | 0.8139 | 1.0868 | 0.9218 |
+| 8 | 1.5053 | 0.8194 | 1.0763 | **0.9262** |
+| 9 | 1.4673 | 0.8201 | 1.0824 | 0.9225 |
+
+---
+
+## 🏆 Best Performance
+
+- **Best Validation Accuracy:** **92.62%**
+- **F1 Score:** 0.9244  
+- **Precision:** 0.9315  
+- **Recall:** 0.9262  
+- Achieved at **Epoch 8**
+
+---
+
+## 📈 Additional Metrics
+
+| Metric | Value |
+|-------|------|
+| Precision | 0.9315 |
+| Recall | 0.9262 |
+| F1 Score | 0.9244 |
+
+---
+
+## ⚙️ Training Details
+
+- Mixed Precision Training (`torch.cuda.amp`)
+- GPU Memory Usage: ~9.3–9.8 GB
+- Training Time per Epoch: ~2.5 hours
+- Evaluation Time per Epoch: ~20 minutes
+- Best model checkpoint saved automatically
+
+---
+
+## 🚀 Usage
+
+### Install Dependencies
+
+```bash
+pip install torch torchvision transformers