---
license: mit
datasets:
- likaixin/IconStack-Captions-48M
- likaixin/IconStack-48M-Pre-Rendered
- starvector/svg-stack
language:
- en
metrics:
- accuracy
base_model:
- laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K
tags:
- art
- icon
model-index:
  - name: IconClip-ViT-L-14
    results:
    - task:
        type: zero-shot-classification
      dataset:
        name: ui-icon-dataset
        type: ui-icon-dataset
      metrics:
        - name: acc@1
          type: accuracy
          value: 80.24
        - name: acc@5
          type: accuracy
          value: 94.74
        
---


# Model Description

A CLIP ViT-B/32 model trained with the [IconStack dataset](https://huggingface.co/datasets/likaixin/IconStack-Captions-48M) using [OpenCLIP](https://github.com/mlfoundations/open_clip).

It scores 80.24% on zero-shot classification on [icon-dataset](https://huggingface.co/datasets/likaixin/ui-icon-dataset).


## Installation
You need to install `open_clip` to use this model:
```bash
pip install open_clip_torch
```

## Icon-to-Text Zero-Shot Classification

```python
import torch
from PIL import Image
import open_clip

CLIP_TEXT_TEMPLATE = "an icon of {}"
ICON_CLASSES = ["add", "close", "play", ...]  # Modify your class names here

model_checkpoint = "<path_to_your_local_model>"
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained=model_checkpoint)
model.eval()
tokenizer = open_clip.get_tokenizer('ViT-B-32')

image = preprocess(Image.open("icon.png")).unsqueeze(0)
text = tokenizer([CLIP_TEXT_TEMPLATE.format(cls) for cls in ICON_CLASSES])

with torch.no_grad(), torch.autocast("cuda"):
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)  # prints something like: [[1., 0., 0., ...]]
```