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Source channel @githubtrending · Post #15365 · Dec 24

#jupyter_notebook DINOv3 offers powerful self-supervised vision models from Meta AI, like ViT up to 7B parameters and ConvNeXt, pretrained on 1.7B web or satellite images. Load them easily via PyTorch Hub, Hugging Face Transformers (v4.56+), or timm (v1.0.20+), with code examples for features, depth, detection, and segmentation. You benefit by using these top-performing, dense features without fine-tuning or labels—saving time and compute for tasks like classification, object detection, and zero-shot analysis on your images. https://github.com/facebookresearch/dinov3

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Venture Village Wall 🦄

@venturevillagewall · Post #3606 · 12/20/2024, 06:41 PM

O3 and O3-Mini Benchmark Breakthroughs The O3 and O3-Mini models showcase state-of-the-art (SOTA) performance with significant leaps in various benchmarks. Results on Frontier Math have jumped from 2% to 25%. The SWE-Bench model achieved a score of 71.7%, while a startup has raised $200 million following results of 13.86%. ELO on Codeforces reached 2727, surpassing most peers globally. Notably, the ARC-AGI model scored 87.5%, breaking a five-year benchmark. Access for security researchers to O3-Mini starts today, with general access available in late January. #O3#O3Mini#SOTA#Benchmarks#AI#ML#Funding#Codeforces#ARC-AGI #FrontierMath#SWE-Bench #ELO#GPQA#AIME#SecurityResearch#TechUpdates#Innovations#Startups#Performance#AIModels