DINOv2 from Meta AI: Data pipeline, model training and results explained
This video is about version 2 of the DINO model (DINO v2 or DINOv2) released by Meta AI in April 2023. It explains the data curation pipeline, the model training procedure going from DINO-v1 to DINO-v2.
Paper title: DINOv2: Learning Robust Visual Features without Supervision
Paper Abstract: The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in
terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model(Dosovitskiy
et al., 2020) with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP (Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
2:12 - Data Processing Pipeline
3:18 - Deduplication process
4:46 - Retrieval (similarity search)
6:10 - DINO-v1 revisited
7:16 - iBOT explained
8:18 - KoLeo Regularization
10:05 - Implementation Efficiency
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