Update README.md

README.md

@@ -1,4 +1,103 @@
 ---
 license: mit
 pipeline_tag: video-classification
----
+---
+
+# cakelens-v5 
+Open-source AI-gen video detection model
+
+Please see the [blog post](https://fangpenlin.com/posts/2025/07/30/open-source-cakelens-v5/) for more details.
+
+## Installation
+
+Install the package with its dependencies:
+
+```bash
+pip install cakelens-v5
+```
+
+## Command Line Interface
+
+The package provides a command line tool `cakelens` for easy video detection:
+
+### Basic Usage
+
+```bash
+# Using Hugging Face Hub (recommended)
+cakelens video.mp4
+
+# Using local model file
+cakelens video.mp4 --model-path model.pt
+```
+
+### Options
+
+- `--model-path`: Path to the model checkpoint file (optional - will load from Hugging Face Hub if not provided)
+- `--batch-size`: Batch size for inference (default: 1)
+- `--device`: Device to run inference on (`cpu`, `cuda`, `mps`) - auto-detected if not specified
+- `--verbose, -v`: Enable verbose logging
+- `--output`: Output file path for results (JSON format)
+
+### Examples
+
+```bash
+# Basic detection (uses Hugging Face Hub)
+cakelens video.mp4
+
+# Using local model file
+cakelens video.mp4 --model-path model.pt
+
+# With custom batch size and device
+cakelens video.mp4 --batch-size 4 --device cuda
+
+# Save results to JSON file
+cakelens video.mp4 --output results.json
+
+# Verbose output
+cakelens video.mp4 --verbose
+```
+
+### Output
+
+The tool provides:
+- Real-time prediction percentages for each label
+- Final mean predictions across all frames
+- Option to save results in JSON format
+- Detailed logging (with `--verbose` flag)
+
+## Programmatic Usage
+
+You can also use the detection functionality programmatically in your Python code:
+
+### Basic Detection
+
+```python
+import pathlib
+from cakelens.detect import Detector
+from cakelens.model import Model
+
+# Create model and load from Hugging Face Hub
+model = Model()
+# load the model weights from Hugging Face Hub
+model.load_from_huggingface_hub()
+# or, if you have a local model file:
+# model.load_state_dict(torch.load("model.pt")["model_state_dict"])
+
+# Create detector
+detector = Detector(
+    model=model,
+    batch_size=1,
+    device="cpu"  # or "cuda", "mps", or None for auto-detection
+)
+
+# Run detection
+video_path = pathlib.Path("video.mp4")
+verdict = detector.detect(video_path)
+
+# Access results
+print(f"Video: {verdict.video_filepath}")
+print(f"Frame count: {verdict.frame_count}")
+print("Predictions:")
+for i, prob in enumerate(verdict.predictions):
+    print(f"  Label {i}: {prob * 100:.2f}%")
+```