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license: apache-2.0 |
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tags: |
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- computer-vision |
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- object-detection |
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- rf-detr |
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- face-detection |
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datasets: |
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- fareselmenshawii/face-detection-dataset |
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metrics: |
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- mAP |
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--- |
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# Model Card for RFDETR-Medium - Face Detection Finetune |
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This model is a fine-tuned version of **RFDETR-Medium** (Real-time Detection Transformer) specifically optimized for the detection of human faces. It leverages a DINOv2 backbone for high-quality feature extraction and a transformer-based head for NMS-free, end-to-end detection. |
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## Model Details |
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### Model Description |
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- **Model type:** Object Detection |
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- **Task:** Human Face Detection |
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- **Finetuned from model:** RFDETR-Medium |
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- **Language(s):** N/A (Computer Vision) |
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- **License:** Apache 2.0 |
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--- |
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## Training Details |
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### Training Hyperparameters |
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The following hyperparameters were used during the fine-tuning process: |
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* **Epochs:** 20 |
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* **Batch Size:** 16 |
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* **Learning Rate:** $1 \times 10^{-4}$ |
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* **Input Image Size:** $576 \times 576$ |
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### Evalutation Results |
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* **mAP@50:** 0.9 |
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* **mAP@5095:** 0.6 |
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### Training Data |
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The model was trained on the Face Detection Dataset (Kaggle), which contains approximately 16,700 images with bounding box annotations for human faces. |
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### Model Sources |
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- **Repository:** [Roboflow RF-DETR GitHub](https://github.com/roboflow/rf-detr) |
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- **Dataset:** [Face Detection Dataset (Kaggle)](https://www.kaggle.com/datasets/fareselmenshawii/face-detection-dataset/data) |
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## Uses |
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### Direct Use |
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This model is designed for **real-time human face detection** in images and video streams. |
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## How to Get Started with the Model |
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```python |
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from rfdetr import RFDETRMedium |
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# Load your fine-tuned weights |
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model = RFDETRMedium(device="cuda", pretrain_weights="rfdetr_medium_face.pth") |
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model.optimize_for_inference() |
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# Run inference |
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results = model.predict("input_image.jpg", threshold=0.5) |
