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
| pipeline_tag: object-detection | |
| tags: | |
| - vision | |
| # Detection Transformers with Assignment | |
| By [Jeffrey Ouyang-Zhang](https://jozhang97.github.io/), [Jang Hyun Cho](https://sites.google.com/view/janghyuncho/), [Xingyi Zhou](https://www.cs.utexas.edu/~zhouxy/), [Philipp Krähenbühl](http://www.philkr.net/) | |
| From the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137). | |
| **TL; DR.** **De**tection **T**ransformers with **A**ssignment (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). |