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---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- graphic
- 2d
- 3d
- image-classifier
- art
---
# **Graphic-Class**
> **Graphic-Class** is a vision model fine-tuned from **google/siglip2-base-patch16-224** for **graphic content moderation**. It uses the **SiglipForImageClassification** architecture to classify graphical images (such as UI designs, 2D game assets, digital art) into **safe** or **problematic** categories.
---
## **Label Space: 2 Classes**
The model classifies each image into one of the following categories:
```
0: bad
1: good
```
* `bad`: images with bad symbols, inappropriate or offensive text, broken UI/UX elements, distorted or harmful designs.
* `good`: plain, safe, or character-rich graphics, such as 2D game elements, educational visuals, or well-structured UI components.
---
## **Install Dependencies**
```bash
pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Graphic-Class" # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "bad",
"1": "good"
}
def classify_graphic(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_graphic,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Graphic Content Classification"),
title="Graphic-Class",
description="Upload a graphic or design asset to classify it as 'good' or 'bad'."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Intended Use**
**Graphic-Class** can be used for:
* **Graphic Content Moderation** – Automatically filter unsafe or visually inappropriate designs in creative pipelines.
* **Game Asset Filtering** – Evaluate textures, objects, or sprites for suitability in game environments.
* **UI/UX Quality Control** – Detect broken or low-quality interface components in design feedback loops.
* **Educational & Kids App Filtering** – Ensure graphics meet safety and design standards for children's content. |