Skip to content
#

zero-shot-classification

Here are 94 public repositories matching this topic...

This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.

  • Updated Jan 30, 2025
  • Jupyter Notebook
text-to-image-eval

Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.

  • Updated Jan 15, 2025
  • Jupyter Notebook

Alternate Implementation for Zero Shot Text Classification: Instead of reframing NLI/XNLI, this reframes the text backbone of CLIP models to do ZSC. Hence, can be lightweight + supports more languages without trading-off accuracy. (Super simple, a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da

  • Updated Apr 5, 2022
  • Python

Improve this page

Add a description, image, and links to the zero-shot-classification topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the zero-shot-classification topic, visit your repo's landing page and select "manage topics."

Learn more