README.md

---
license: apache-2.0
tags:
- object-detection
- rfdetr
- computer-vision
library_name: rfdetr
pipeline_tag: object-detection
---

# RF-DETR MEDIUM Model

This is an RF-DETR (Real-time Transformer-based Object Detector) model fine-tuned on a custom dataset.

## Model Details

- **Model Architecture**: RFDETRMedium
- **Framework**: RF-DETR (PyTorch)
- **Image Resolution**: 640x640
- **Training Epochs**: 50
- **Batch Size**: 4 (with gradient accumulation steps: 4)

## Classes

The model is trained to detect the following classes:
`drone`

## Usage

### Using RF-DETR Library (Recommended)

```python
from rfdetr import RFDETRMedium
from PIL import Image

# Load the model
model = RFDETRMedium(
    pretrain_weights="hf://rujutashashikanjoshi/rfdetr-drone-detection-0205/checkpoint_best_total.pth"
)
model.optimize_for_inference()

# Run inference
image = Image.open("your_image.jpg")
detections = model.predict(image, threshold=0.5)

print(f"Found {len(detections)} detections")
```

### Using PyTorch Directly

```python
import torch
from huggingface_hub import hf_hub_download

# Download the model weights
model_path = hf_hub_download(
    repo_id="rujutashashikanjoshi/rfdetr-drone-detection-0205",
    filename="pytorch_model.bin"
)

# Load the state dict
state_dict = torch.load(model_path, map_location='cpu')
```

## Training Details

- **Optimizer**: AdamW
- **Learning Rate Schedule**: Cosine with warmup
- **Data Format**: COCO JSON
- **Framework Version**: rfdetr>=1.2.1

## Files in this Repository

- `checkpoint_best_total.pth`: Original RF-DETR checkpoint (best model)
- `pytorch_model.bin`: Standard PyTorch weights for compatibility
- `config.json`: Model configuration
- `class_names.txt`: List of detection classes
- `results.json`: Training results and metrics
- `metrics_plot.png`: Visualization of training metrics
- `log.json`: Full training log

## License

This model is released under the Apache 2.0 License.