YOLOS-Fashionpedia
| Property | Value |
|---|---|
| Author | valentinafeve |
| Framework | PyTorch |
| Task | Object Detection |
| Dataset | Fashionpedia |
What is yolos-fashionpedia?
YOLOS-Fashionpedia is a specialized computer vision model designed for fashion object detection. Built on the YOLOS (You Only Look at One Sequence) architecture, this model has been fine-tuned specifically for identifying and localizing 46 different fashion-related categories, ranging from clothing items to accessories and decorative elements.
Implementation Details
The model leverages the transformer-based YOLOS architecture and has been fine-tuned on the Fashionpedia dataset. It implements object detection capabilities through PyTorch and can be easily integrated into production environments through Inference Endpoints.
- Built on YOLOS architecture utilizing transformer technology
- Fine-tuned specifically for fashion detection tasks
- Supports 46 distinct fashion categories
- Implemented in PyTorch with production-ready capabilities
Core Capabilities
- Detects clothing items (shirts, pants, dresses, etc.)
- Identifies accessories (glasses, hats, bags, etc.)
- Recognizes clothing details (pockets, zippers, collars)
- Detects decorative elements (applique, beads, bows, etc.)
- Supports real-time fashion object detection
Frequently Asked Questions
Q: What makes this model unique?
The model's specialization in fashion object detection, combined with its comprehensive category coverage (46 classes) and transformer-based architecture, makes it particularly suited for detailed fashion analysis tasks. Its significant download count (16,722+) demonstrates its practical utility in the field.
Q: What are the recommended use cases?
This model is ideal for e-commerce platforms, fashion analytics applications, virtual styling systems, and automated fashion cataloging. It can be used for tasks such as automated product tagging, visual search, and detailed fashion item analysis.
