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---
tags:
- object-detection
- rf-detr
- package-segmentation
- computer-vision
library_name: rfdetr
license: apache-2.0
---
# RF-DETR Package Detection Model
This model is a fine-tuned **RF-DETR Medium** model for package/box detection, trained on a custom dataset.
## Model Description
- **Model Type:** RF-DETR (Real-time DETR for object detection)
- **Base Model:** RF-DETR Medium
- **Task:** Object Detection (Package/Box Segmentation)
- **Training Data:** Custom dataset
- **Classes:** 2 class(es)
## Training Details
- **Epochs:** N/A
- **Batch Size:** 16
- **Learning Rate:** 5e-05
- **Input Resolution:** 576x576
## Performance
Training metrics available in the model repository.
## Usage
### Installation
```bash
pip install rfdetr torch torchvision
```
### Loading the Model
```python
import torch
from rfdetr import RFDETRMedium
from PIL import Image
# Load model
model = RFDETRMedium()
checkpoint = torch.load("checkpoint_best_total.pth", map_location='cpu')
model.model.load_state_dict(checkpoint['model'])
model.model.eval()
# Run inference
image = Image.open("path/to/image.jpg")
results = model.predict(image)
```
### API Usage (with Inference Endpoints)
Once deployed as an Inference Endpoint:
```python
import requests
from PIL import Image
import io
API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/rf-detr-box-segmentation"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
# Send image
with open("image.jpg", "rb") as f:
data = f.read()
response = requests.post(API_URL, headers=headers, data=data)
results = response.json()
```
## Model Details
- **Developed by:** Your Name
- **Model date:** 1762288019.2803671
- **Framework:** PyTorch
- **License:** Apache 2.0
## Citation
```bibtex
@software{rfdetr2024,
title = {RF-DETR: Real-time DETR},
author = {Roboflow},
year = {2024},
url = {https://github.com/roboflow/rf-detr}
}
```
## Limitations
This model is trained on a specific package detection dataset and may not generalize to all object detection tasks.
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