# Object detection

TF-ID is an object detection model series created by Yifei Hu for extracting tables and figures from academic papers. These models are fine-tuned based on the microsoft/Florence-2 checkpoint, offering versions with or without title text. Their aim is to enhance the accessibility and processing efficiency of information in academic literature.

YOLO-World is an advanced real-time open vocabulary object detector based on the You Only Look Once (YOLO) series of detectors. It enhances open vocabulary detection capabilities through visual-language modeling and pre-training on a large dataset. It employs a novel reparameterizable visual-language path aggregation network (RepVL-PAN) and region-text contrastive loss, promoting interaction between visual and linguistic information. YOLO-World efficiently detects a variety of objects in a zero-shot manner, exhibiting high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP and 52.0 FPS on a V100, outperforming many state-of-the-art methods in both accuracy and speed. Moreover, fine-tuned YOLO-World demonstrates outstanding performance on multiple downstream tasks, including object detection and open vocabulary instance segmentation.