ImageNet21K
vision_transformer
| ImageNet21K | vision_transformer | |
|---|---|---|
| 1 | 7 | |
| 695 | 9,365 | |
| 2.9% | 3.0% | |
| 10.0 | 5.5 | |
| over 1 year ago | 2 months ago | |
| Python | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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ImageNet21K
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
ViT-B/32, using the ImageNet-21k dataset
vision_transformer
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Can I use CLIP to tag my picture collection?
And one last thing, should I even be thinking of using CLIP for these tasks when Google has released a better model here: https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md
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When the client's management is happy but their dev team is a pain
Google's vision transformers are type hinted.
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
We’re going to look at a model that Open AI has trained with a broad multilingual dataset: The xlm-roberta-base-ViT-B-32 CLIP model, which uses the ViT-B/32image encoder, and the XLM-RoBERTa multilingual language model. Both of these are pre-trained:
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[R] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
JAX Code: https://github.com/google-research/vision_transformer
- [D] (Paper Overview) MLP-Mixer: An all-MLP Architecture for Vision
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[P] Animesion: a framework, for anime (and related) character recognition. It uses Vision Transformers trained on a subset of Danbooru2018, that we rebranded as DAF:re, and can classify a given image into one of more than 3000 characters! Source code and checkpoints included.
For this project I used the pretrained models released by Google in Jax, using this particular PyTorch custom implementation. Those were pretrained on ImageNet21k with 14 M images among 21 K classes. Then yes I finetune on two datasets: one with 15 K images and 170 characters, and one with 3 K characters and almost 500 K images.
- Short term memory solutions for video tasks?
What are some alternatives?
OFA - Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
LMOps - General technology for enabling AI capabilities w/ LLMs and MLLMs
nerfstudio - A collaboration friendly studio for NeRFs
Fashion12K_german_queries
mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
TorchSharp - A .NET library that provides access to the library that powers PyTorch.
docarray - Represent, send, store and search multimodal data
fashion-200k - Fashion 200K dataset used in paper "Automatic Spatially-aware Fashion Concept Discovery."
typeshed - Collection of library stubs for Python, with static types