IDEA-Research

Model · Zero shot object detection

grounding-dino-base

IDEA-Research

The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. alt="drawing" width="600"/> You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data). Here's how to use the model for zero-shot object detection

Model · Zero shot object detection

grounding-dino-tiny

IDEA-Research

The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. alt="drawing" width="600"/> You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data). Here's how to use the model for zero-shot object detection

Model · Object detection

dab-detr-resnet-50

IDEA-Research

This is the model card of a transformers model that has been pushed on the Hub. Use the code below to get started with the model. This should output The DAB-DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively. Following Deformable DETR and Conditional DETR, we use 300 anchors as queries. We select 300 predicted boxes and labels with the largest classification logits for evaluation as well. We also use focal loss (Lin et al., 2020) with α = 0.25, γ = 2 for classification. The same loss terms are used in bipartite matching and final loss calculating, but with different coefficients. Classification loss…

Model · Image and text to text

Rex-Omni

IDEA-Research

This model is Rex-Omni, a 3B-parameter Multimodal Large Language Model (MLLM) presented in the paper "Detect Anything via Next Point Prediction". It is compatible with the Hugging Face transformers library and is licensed under the IDEA License 1.0. src="https://img.shields.io/badge/RexOmni-Website-BADFDB?style=flat-square&logo=deno&logoColor=violet&color=BADFDB" alt="RexThinker Website" src="https://img.shields.io/badge/RexOmni-Paper-Red%25red?logo=arxiv&logoColor=red&color=yellow" alt="RexThinker Paper on arXiv" src="https://img.shields.io/badge/RexOmni-Weight-orange?logo=huggingface&logoColor=yellow" alt="RexThinker weight on Hugging Face"…