README.md

metadata

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.

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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.

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Install Dependencies

pip install -q transformers torch pillow gradio

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Inference Code

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.