ZhangYuanhan-AI/NOAH
[TPAMI] Searching prompt modules for parameter-efficient transfer learning.
This project helps machine learning engineers or researchers efficiently adapt large pre-trained vision models to new, specific visual tasks. You provide a pre-trained model and new image datasets, and it automatically finds the best lightweight configuration (prompt modules) to achieve high performance on your specific tasks without extensive manual tuning. This is ideal for those working on computer vision applications.
238 stars. No commits in the last 6 months.
Use this if you need to fine-tune large vision models for many different image classification or recognition tasks and want to automate the optimization of adaptation modules.
Not ideal if you are looking for a general-purpose image labeling tool or a ready-to-use API for standard computer vision tasks without custom model adaptation.
Stars
238
Forks
12
Language
Python
License
MIT
Category
Last pushed
Dec 08, 2023
Commits (30d)
0
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