GitHub - LightDXY/FT-CLIP: CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
![PDF] Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation | Semantic Scholar PDF] Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/7f71875f8214dffa4f3276da123c4990a6d437cc/8-Table2-1.png)
PDF] Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation | Semantic Scholar
pharmapsychotic on Twitter: "#stablediffusion2 uses the OpenCLIP ViT-H model trained on the LAION dataset so it knows different things than the OpenAI ViT-L we're all used to prompting. To help out with
![CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet – arXiv Vanity CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet – arXiv Vanity](https://media.arxiv-vanity.com/render-output/7111142/x1.png)
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet – arXiv Vanity
![Image-text similarity score distributions using CLIP ViT-B/32 (left)... | Download Scientific Diagram Image-text similarity score distributions using CLIP ViT-B/32 (left)... | Download Scientific Diagram](https://www.researchgate.net/publication/370338853/figure/fig4/AS:11431281154074595@1682653020748/Image-text-similarity-score-distributions-using-CLIP-ViT-B-32-left-and-ViT-L-14-right_Q320.jpg)