rtic-gcn-pytorch VS OFA

Compare rtic-gcn-pytorch vs OFA and see what are their differences.

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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rtic-gcn-pytorch OFA
2 3
20 2,331
- 1.0%
0.0 2.8
over 2 years ago 15 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.

rtic-gcn-pytorch

Posts with mentions or reviews of rtic-gcn-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-25.

What are some alternatives?

When comparing rtic-gcn-pytorch and OFA you can also consider the following projects:

clean-code-dotnet - :bathtub: Clean Code concepts and tools adapted for .NET

ImageNet21K - Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper

BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

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

clean-code-javascript - :bathtub: Clean Code concepts adapted for JavaScript

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

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

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