DINOv2
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- Опубликовано: 5 окт 2024
- In this stream we look at Meta's latest research: DINOv2 the second version of the self-supervised foundational CV model.
github.com/fac...
arxiv.org/pdf/...
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this is great. as a master-student who would probably understand next to nothing on their own from these latest cutting-edge ML research papers, this helps A LOT. Looking forward to your future vids and streams :-)
I really enjoyed your overview of the paper. I'd also be interested in paper reviews of "tips and tricks", comparing certain techniques such as mixed-precision across a variety of CV tasks. While things like increasing batch size work for large companies, techniques that work for consumer grade hardware are more applicable even for researchers or grad students.
Something very funny happened. My neighboor cat comes to visit me almost daily. I started hearing the "Meow" on the speakers and I tought it was the neighboor's cat. I actually stopped the video twice to go search for the cat in front of the door.
Say hello to you cat, and thanks for the video.
I was just looking for it. You gave me amazing understanding
The first person I've seen to actually use Nvidias Eye Contact :D
Very nice video. Thank you! 🙏
Nice, I enjoy listening to that.
Great video. Love your videos. Glad I found your channel.
Very cool stream!
*Summary: DINOv2 Paper Review*
*DINOv2: A Self-Supervised Foundation Model for Computer Vision*
* *Focus (**0:57**):* Training a large-scale, self-supervised computer vision model called DINOv2.
* *Goal (**4:40**):* Develop a model that generates versatile visual features, usable for various tasks without fine-tuning.
* *Key Ideas:*
* *Data Curation (**5:22**):* Training on a curated dataset of 142 million images (LVD-142M) leads to superior performance compared to uncurated data of the same size.
* *Self-Supervised Learning (**11:09**):* Employs a combination of existing self-supervised learning methods (DINO, iBOT) with new techniques for stabilization and acceleration.
* *Large Model and Data Scale (**6:12**):* Trains a Vision Transformer (ViT) with 1 billion parameters on a massive dataset, demonstrating the importance of scale for self-supervised learning.
* *Model Distillation (**7:44**):* Distills smaller models from the largest trained model, leading to performance improvements compared to training from scratch.
* *High-Resolution Training (**38:56**):* Demonstrates the importance of high-resolution training for pixel-level tasks like segmentation and depth estimation. Introduces a curriculum of training on low resolution and then high resolution.
* *Results:*
* *Competitive Performance (**21:54**):* DINOv2 achieves competitive performance compared to the best openly available weakly-supervised models, including OpenCLIP, across various benchmarks.
* *Strong Generalization (**11:40**):* Outperforms other self-supervised models on domain generalization benchmarks, demonstrating strong transferability to unseen data.
* *Emergent Properties (**12:25**):* Exhibits emergent properties like understanding object parts and scene geometry, similar to how LLMs develop emergent capabilities.
* *Technical Contributions (**22:21**):*
* Automatic data curation pipeline.
* Techniques for stabilizing and accelerating training (31:59), including:
* Fast and memory-efficient attention.
* Efficient stochastic depth.
* Fully sharded data parallelism.
* Detailed ablation studies to validate different components of the approach (54:37).
* *Impact (**1:53:02**):* DINOv2 pushes the boundaries of self-supervised learning in computer vision and provides a powerful new tool for researchers and practitioners.
*Noteworthy Observations:*
* The paper emphasizes the importance of curated data and large-scale training for achieving high-quality representations in self-supervised learning.
* Model distillation emerges as a promising technique for efficiently creating smaller, high-performing models.
* The authors acknowledge the potential for even greater emergent properties with further scaling of model and data size.
* Facebook AI Research's openness in sharing their model, code, and training details is commendable.
i used gemini 1.5 pro to summarize the transcript
Love the explanation, do you think it can be used in the wild?
Who has the bravery and the resources?
Great video by the way
cat.. 🐱
ty
Have you by any chance added the glasses artificially ?
Nvidia broadcast
@@hu-po why though ? :D that's brilliant!
meow,meow,meow,meow,meow XD
meow
Gato model is trying to say some interesting info lol
Man, the eyes are throwing me off every time you look up! I am assuming you are using that thing that makes you keep eye contact. Turn it off. I try to pretend not to look at you, and every time I do, I stop watching!
tes.. ing, teeslay, parlay