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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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ФГБУН Институт высокотемпературной электрохимии УрО РАН

@ihteubras · Post #419 · 11/29/2023, 04:29 AM

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Опубликована новая работа 🟠 2023 🟠 V. 10 🟠 Issue 4 🟠 Art. 202310409 🟠 Article 🟠 📜 Features of forming a low-temperature cubic Li7La3Zr2O12 film by tape casting 👩‍🎓👨‍🎓 E. Lyalin (http://orcid.org/0000-0002-0539-506X), L. Pershina (http://orcid.org/0000-0003-1759-5234), E. Il’ina, K. Druzhinin (http://orcid.org/0000-0002-8947-8208), S. Belyakov (https://orcid.org/0000-0001-9237-8307) 🏛 Institute of High Temperature Electrochemistry, UB RAS, https://ihte.ru 🏛 Ural Federal University, https://urfu.ru/en 📚#Li7La3Zr2O12#LIB#lithium#conductivity#films#tapecasting#allSSB 🔗https://doi.org/10.15826/chimtech.2023.10.4.09 https://www.chimicatechnoacta.ru/index.php/chimtech/article/view/7161

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