Structured 3D Latents for Scalable and Versatile 3D Generation
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2412.01506
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de-Rodrigo
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Synthetic Datasets, Multimodal LLMs, Computer Vision
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Structured 3D Latents for Scalable and Versatile 3D Generation
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The
Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
* test_vit ( timm/test_vit.r160_in1k)
* test_efficientnet ( timm/test_efficientnet.r160_in1k)
* test_byobnet ( timm/test_byobnet.r160_in1k, a mix of resnet, darknet, effnet/regnet like blocks)
They are < 0.5M params, insanely fast and originally intended for unit testing w/ real weights. They have awful ImageNet top-1, it's rare to have anyone bother to train a model this small on ImageNet (the classifier is roughly 30-70% of the param count!). However, they are FAST on very limited hadware and you can fine-tune them well on small data. Could be the model you're looking for?
timm leaderboard
timm/leaderboard has been updated with the ability to select different hardware benchmark sets: RTX4090, RTX3090, two different CPUs along with some NCHW / NHWC layout and torch.compile (dynamo) variations. Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
* test_vit ( timm/test_vit.r160_in1k)
* test_efficientnet ( timm/test_efficientnet.r160_in1k)
* test_byobnet ( timm/test_byobnet.r160_in1k, a mix of resnet, darknet, effnet/regnet like blocks)
They are < 0.5M params, insanely fast and originally intended for unit testing w/ real weights. They have awful ImageNet top-1, it's rare to have anyone bother to train a model this small on ImageNet (the classifier is roughly 30-70% of the param count!). However, they are FAST on very limited hadware and you can fine-tune them well on small data. Could be the model you're looking for?
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👷🏽♀️📚🔨 Announcing the Foundation Model Development Cheatsheet!
My first 🤗Post🤗 ever to announce the release of a fantastic collaborative resource to support model developers across the full development stack: The FM Development Cheatsheet available here: https://fmcheatsheet.org/
The cheatsheet is a growing database of the many crucial resources coming from open research and development efforts to support the responsible development of models. This new resource highlights essential yet often underutilized tools in order to make it as easy as possible for developers to adopt best practices, covering among other aspects:
🧑🏼🤝🧑🏼 data selection, curation, and governance;
📖 accurate and limitations-aware documentation;
⚡ energy efficiency throughout the training phase;
📊 thorough capability assessments and risk evaluations;
🌏 environmentally and socially conscious deployment strategies.
We strongly encourage developers working on creating and improving models to make full use of the tools listed here, and to help keep the resource up to date by adding the resources that you yourself have developed or found useful in your own practice 🤗
Congrats to all the participants in this effort for the release! Read more about it from:
@Shayne - https://twitter.com/ShayneRedford/status/1763215814860186005
@hails and @stellaathena - https://blog.eleuther.ai/fm-dev-cheatsheet/
@alon-albalak - http://nlp.cs.ucsb.edu/blog/a-new-guide-for-the-responsible-development-of-foundation-models.html
And also to @gabrielilharco @sayashk @kklyman @kylel @mbrauh @fauxneticien @avi-skowron @Bertievidgen Laura Weidinger, Arvind Narayanan, @VictorSanh @Davlan @percyliang Rishi Bommasani, @breakend @sasha 🔥
My first 🤗Post🤗 ever to announce the release of a fantastic collaborative resource to support model developers across the full development stack: The FM Development Cheatsheet available here: https://fmcheatsheet.org/
The cheatsheet is a growing database of the many crucial resources coming from open research and development efforts to support the responsible development of models. This new resource highlights essential yet often underutilized tools in order to make it as easy as possible for developers to adopt best practices, covering among other aspects:
🧑🏼🤝🧑🏼 data selection, curation, and governance;
📖 accurate and limitations-aware documentation;
⚡ energy efficiency throughout the training phase;
📊 thorough capability assessments and risk evaluations;
🌏 environmentally and socially conscious deployment strategies.
We strongly encourage developers working on creating and improving models to make full use of the tools listed here, and to help keep the resource up to date by adding the resources that you yourself have developed or found useful in your own practice 🤗
Congrats to all the participants in this effort for the release! Read more about it from:
@Shayne - https://twitter.com/ShayneRedford/status/1763215814860186005
@hails and @stellaathena - https://blog.eleuther.ai/fm-dev-cheatsheet/
@alon-albalak - http://nlp.cs.ucsb.edu/blog/a-new-guide-for-the-responsible-development-of-foundation-models.html
And also to @gabrielilharco @sayashk @kklyman @kylel @mbrauh @fauxneticien @avi-skowron @Bertievidgen Laura Weidinger, Arvind Narayanan, @VictorSanh @Davlan @percyliang Rishi Bommasani, @breakend @sasha 🔥
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