README.md

---
license: apache-2.0
language:
- en
---
# Model Quantization Notebook

This notebook converts a pre-trained Keras violence detection model into TensorFlow Lite (TFLite) format using three different quantization strategies, making it suitable for deployment on edge/mobile devices.

---

## Overview

| Property | Details |
|---|---|
| **Framework** | TensorFlow / TFLite |
| **Base Model** | `modelv2.keras` — a Keras video violence detection model |
| **Input Shape** | `(1, 16, 224, 224, 3)` — batch × frames × height × width × channels |
| **Architecture** | CNN + LSTM (contains dynamic LSTM loops) |
| **Platform** | Kaggle (GPU hidden to avoid CuDNN conflicts) |

---

## Quantization Methods

### A — Dynamic Range Quantization
- **Output file:** `model_dynamic_quant.tflite`
- Quantizes weights from float32 to int8 at **conversion time**.
- Activations are quantized **dynamically** at inference time.
- Fastest to convert; no calibration data required.
- Good balance between size reduction and accuracy.

### B — Float16 Quantization
- **Output file:** `model_fp16_quant.tflite`
- Reduces weight precision from float32 to **float16**.
- Ideal for GPU-accelerated edge devices that support fp16 natively.
- Smaller model size with minimal accuracy loss.

### C — Full Integer (INT8) Quantization
- **Output file:** `model_full_int8.tflite`
- Quantizes **both weights and activations** to int8.
- Requires a **representative dataset** for calibration (currently uses random dummy data — replace with real video samples for best results).
- Input and output tensors are also forced to int8.
- Smallest model size; best suited for CPU-only or microcontroller deployment.

---

## Requirements

```
tensorflow
numpy
```

---

## Usage

### 1. Load the Base Model
```python
import tensorflow as tf

tf.config.set_visible_devices([], 'GPU')  # Hide GPU to avoid CuDNN issues
model = tf.keras.models.load_model('path/to/modelv2.keras')
```

### 2. Run Quantization
Open and run the notebook cells in order:
1. **Cell 1–2** — Load the model
2. **Cell 3–4** — Dynamic range quantization → `model_dynamic_quant.tflite`
3. **Cell 5–6** — Float16 quantization → `model_fp16_quant.tflite`
4. **Cell 7–8** — Full INT8 quantization → `model_full_int8.tflite`

---

## Important Notes

- **Representative dataset:** The INT8 quantization cell uses random dummy data for calibration. For production use, replace `dummy_data` in `representative_data_gen()` with real video frames from your training set to get accurate quantization ranges.

- **LSTM compatibility flags:** The model contains dynamic LSTM loops. The following flags are set in all conversion paths to prevent conversion failures:
  ```python
  converter.target_spec.supported_ops = [
      tf.lite.OpsSet.TFLITE_BUILTINS,
      tf.lite.OpsSet.SELECT_TF_OPS
  ]
  converter._experimental_lower_tensor_list_ops = False
  ```

- **Static input shape:** The INT8 path uses `tf.function` with a `tf.TensorSpec` to lock the input shape to `(1, 16, 224, 224, 3)` before conversion — this is required for correct INT8 LSTM quantization.

---

## Output Files

| File | Method | Precision |
|---|---|---|
| `model_dynamic_quant.tflite` | Dynamic Range | Weights: INT8, Activations: float32 |
| `model_fp16_quant.tflite` | Float16 | Weights & Activations: float16 |
| `model_full_int8.tflite` | Full Integer | Weights & Activations: INT8 |