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DETR Object Detection Fashionpedia-finetuned is a specialized version of the DETR (DEtection TRansformer) model, adapted for fashion object detection. It leverages the transformer architecture to achieve state-of-the-art performance in identifying and localizing objects within images, specifically tailored for fashion-related items.
• Highly Accurate Detection: Fine-tuned on the Fashionpedia dataset to provide precise detection of fashion items.
• Comprehensive Fashion Coverage: Supports detection of a wide range of fashion categories, including clothing, accessories, and more.
• Real-Time Processing: Optimized for efficient inference, making it suitable for real-world applications.
• Transformer-Based Architecture: Utilizes self-attention mechanisms for robust object detection.
• Cross-Device Compatibility: Can be deployed on multiple platforms, including mobile and desktop.
Example usage:
model = torchvision.models.detection.DETR()
model.load_state_dict(torch.load("fashionpedia_finetuned_weights.pth"))
What type of objects can DETR Object Detection Fashionpedia-finetuned detect?
It is specifically fine-tuned to detect fashion-related items, such as clothing, accessories, and footwear, with high accuracy.
What datasets was DETR Object Detection Fashionpedia-finetuned trained on?
The base DETR model was trained on COCO, and it was further fine-tuned on the Fashionpedia dataset for specialized fashion object detection.
Can DETR Object Detection Fashionpedia-finetuned work with low-resolution images?
Yes, it can process low-resolution images, but detection accuracy may be reduced compared to high-resolution inputs.