Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models.
Abstract: In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of ...
Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...
This project showcases a sophisticated pipeline for object detection and segmentation using a Vision-Language Model (VLM) and the Segment Anything Model 2 (SAM2). The core idea is to leverage the ...
Researchers at Queen Mary University of London and University College London have found that humans can detect objects buried in sand without directly touching them. The discovery challenges the ...
Abstract: Uncrewed aerial vehicle (UAV) aerial visible–infrared [RGB-thermal (RGBT)] object detection has been widely applied in fields such as military operations and rescue missions. However, ...
2 Methodology 2.1 Overall framework design YOLO11 (Khanam and Hussain, 2024, preprint) is a new generation of object detection algorithm developed by Ultralytics based on the YOLOv8 architecture. It ...
We use Python 3.8, PyTorch 1.13.1 (CUDA 11.7 build). The codebase is built on Detectron. conda create -n wsco python=3.8 Conda activate wsco conda install pytorch==1.12.1 torchvision==0.13.1 ...
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