OatmealLiu/FineR
[ICLR'24] Democratizing Fine-grained Visual Recognition with Large Language Models
This project helps scientists and researchers in fields like biology or zoology identify specific species or variants of objects from images. It takes a small set of example images and uses language models to discover and describe the subtle visual differences. The output is a highly accurate categorization of fine-grained images, even for categories that were not explicitly labeled beforehand.
190 stars. No commits in the last 6 months.
Use this if you need to classify images into very specific, nuanced categories (like different bird species or car models) without extensive manual annotation or predefined category names.
Not ideal if you are looking for broad object detection or classification into general categories, or if you don't have a few example images per fine-grained category for discovery.
Stars
190
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 15, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/OatmealLiu/FineR"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
scaleapi/llm-engine
Scale LLM Engine public repository
AGI-Arena/MARS
The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models
modelscope/easydistill
a toolkit on knowledge distillation for large language models
AGI-Edgerunners/LLM-Adapters
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient...
Wang-ML-Lab/bayesian-peft
Bayesian Low-Rank Adaptation of LLMs: BLoB [NeurIPS 2024] and TFB [NeurIPS 2025]