Instructions to use jozhang97/deta-swin-large-o365 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jozhang97/deta-swin-large-o365 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jozhang97/deta-swin-large-o365")# Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("jozhang97/deta-swin-large-o365", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
pipeline_tag: object-detection
tags:
- vision
Detection Transformers with Assignment
By Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl
From the paper NMS Strikes Back.
TL; DR. Detection Transformers with Assignment (DETA) re-introduce IoU assignment and NMS for transformer-based detectors. DETA trains and tests comparibly as fast as Deformable-DETR and converges much faster (50.2 mAP in 12 epochs on COCO).