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Detr Resnet 50 Dc5 Fashionpedia Finetuned

Developed by sergiopaniego
DETR is a Transformer-based object detection model that handles detection tasks in an end-to-end manner, eliminating the need for complex post-processing steps.
Downloads 57
Release Time : 9/2/2024

Model Overview

DETR is an innovative object detection architecture that combines the strengths of CNNs and Transformers, capable of directly predicting a fixed set of bounding boxes and class labels.

Model Features

End-to-end detection
Directly outputs detection results without complex post-processing steps (e.g., NMS).
Transformer architecture
Utilizes the global attention mechanism of Transformers to process visual features.
Simple and efficient
Simpler architecture and more unified training process compared to traditional detectors.

Model Capabilities

Object detection
Multi-category recognition
Bounding box prediction

Use Cases

Fashion industry
Fashion item detection
Detects and classifies various items and clothing in fashion images
Performs well on the Fashionpedia dataset
Retail
Product recognition
Automatically identifies products in retail scenarios
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