Update README.md

README.md

@@ -2,6 +2,123 @@
 license: apache-2.0
 base_model:
 - google/siglip2-base-patch16-224
+library_name: transformers
 ---
 
 ![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/PxMC3xQE3LdbpfWzv4MBD.png)
+
+# **ImageShield-SUPER-90M**
+
+> **ImageShield-SUPER-90M** is a vision-language image classification model based on **google/siglip2-base-patch16-224**, trained on **100K samples from the ImageShield-Guardrail Safe and Unsafe Images dataset**. Built on the **SiglipForImageClassification** architecture, the model is designed to classify visual content as **Safe** or **Unsafe** for content moderation and media filtering.
+
+> [!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)
+
+## **Label Space: 2 Classes**
+
+The model classifies each image into one of the following content categories:
+
+```text
+Class 0: "Safe"
+Class 1: "Unsafe"
+```
+
+## **Install Dependencies**
+
+```bash
+pip install transformers torch torchvision 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/ImageShield-SUPER-90M"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to Label mapping
+id2label = {
+    "0": "Safe",
+    "1": "Unsafe"
+}
+
+def classify_image(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_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(
+        num_top_classes=2,
+        label="Predicted Content Type"
+    ),
+    title="ImageShield-SUPER-90M",
+    description="Classifies images as Safe or Unsafe."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+## **Intended Use**
+
+This model is intended for applications such as:
+
+* **Content Moderation:** Identify unsafe visual content.
+* **Parental Controls:** Support AI-based media filtering.
+* **Dataset Preprocessing:** Categorize and filter safe and unsafe images.
+* **Online Platforms:** Assist with content safety and upload moderation.
+* **AI Image Applications:** Provide an additional safety layer for image generation and editing workflows.
+
+## **Classification Report**
+
+### Training vs Evaluation Loss / Accuracy
+
+![Training vs Evaluation Loss and Accuracy](assets/training_eval_graph.png)
+
+### Precision / Recall / F1-score per Class
+
+![Per-Class Precision, Recall, and F1-score](assets/classification_report_bar.png)
+
+### Confusion Matrix
+
+![Confusion Matrix](assets/confusion_matrix.png)
+
+### Test Set Class Distribution
+
+![Test Set Class Distribution](assets/class_distribution_pie.png)
+
+### Overall Prediction Accuracy
+
+![Overall Prediction Accuracy](assets/prediction_accuracy_pie.png)
+
+### Misalignment Distribution by True Class
+
+![Misalignment Distribution by True Class](assets/misalignment_distribution_pie.png)
+
+## **Acknowledgements**
+
+* **[Transformers](https://huggingface.co/docs/transformers/en/index)**: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.
+
+* **[SigLIP 2](https://huggingface.co/papers/2502.14786)**: Multilingual vision-language encoders with improved semantic understanding, localization, and dense feature representations.