appy-mod-beta1

appy-mod-beta1 is a vision-language encoder model fine-tuned from siglip2-base-patch16-224 for binary image classification. The model is trained to perform game content moderation, specifically classifying visual content as either safe (good) or unsafe (bad). It utilizes the SiglipForImageClassification architecture.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Classification Report:
              precision    recall  f1-score   support

         bad     0.9763    0.9140    0.9441      1755
        good     0.9279    0.9803    0.9534      1983

    accuracy                         0.9492      3738
   macro avg     0.9521    0.9471    0.9487      3738
weighted avg     0.9506    0.9492    0.9490      3738

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Label Space: 2 Classes

Class 0: bad (Unsafe content)
Class 1: good (Safe content)

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Install Dependencies

pip install transformers torch pillow gradio hf_xet

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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 = "KarteeMonkey/appy-mod-beta1"  # Update this if using a different path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "bad",
    "1": "good"
}

def classify_watermark(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_watermark,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Game Anomaly Detection"),
    title="Game Anomaly Detection SigLIP2",
    description="Upload an image to detect whether it contains a anomaly."
)

if __name__ == "__main__":
    iface.launch()

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Intended Use

appy-mod-beta1 is designed for:

• Game Content Moderation – Automated moderation of user-generated or in-game visual content.
• Parental Control Tools – Supports identifying unsafe or inappropriate content in children’s games.
• Online Game Platforms – Enables scalable and automatic screening of images uploaded by users.
• Community Safety – Helps maintain safe and compliant visual environments in multiplayer games and forums.
• AI Moderation Research – A sample project for applying vision-language models to safety-critical applications.