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---
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.
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