---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
datasets:
- KarteeMonkey/Demo
tags:
- Game
- Moderation
- art
- code
---

![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/yChKP0JSFHLs0wiHD9hFf.png)

# 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.

> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)

```py
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
```

![Untitled.png](https://cdn-uploads.huggingface.co/production/uploads/683ff3d6ed4395669d10d6d5/EEX_muJTmjfRLtCIqt2Cc.png)

---

## Label Space: 2 Classes

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

---

## Install Dependencies

```bash
pip install transformers torch pillow gradio hf_xet
```

---

## Inference Code

```python
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()
```

---

## 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.