ImageNet21K VS OFA

Compare ImageNet21K vs OFA and see what are their differences.

ImageNet21K

Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper (by Alibaba-MIIL)

OFA

Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (by OFA-Sys)
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ImageNet21K OFA
1 3
695 2,331
2.9% 1.2%
10.0 2.8
over 1 year ago 21 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ImageNet21K

Posts with mentions or reviews of ImageNet21K. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-22.

What are some alternatives?

When comparing ImageNet21K and OFA you can also consider the following projects:

vision_transformer

GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

LMOps - General technology for enabling AI capabilities w/ LLMs and MLLMs

ONE-PEACE - A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities

Fashion12K_german_queries

MAGIC - Language Models Can See: Plugging Visual Controls in Text Generation

mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model

UPop - [ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

docarray - Represent, send, store and search multimodal data