Update README.md

README.md

@@ -20,7 +20,7 @@ tags:
 
 # **siglip2-x384-explicit-content**
 
-> **siglip2-x384-explicit-content** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-384** for **multi-class image classification**. Based on the **SiglipForImageClassification** architecture, this model is designed to detect and categorize various forms of visual content, from safe to explicit, making it ideal for content moderation and media filtering.
+> **siglip2-x256p32-explicit-content** is a vision-language encoder model fine-tuned from **siglip2-base-patch32-256** for **multi-class image classification**. Based on the **SiglipForImageClassification** architecture, this model is designed to detect and categorize various forms of visual content, from safe to explicit, making it ideal for content moderation and media filtering.
 
 ---
 
@@ -55,7 +55,7 @@ from PIL import Image
 import torch
 
 # Load model and processor
-model_name = "prithivMLmods/siglip2-x384-explicit-content"  # Replace with your HF model path if needed
+model_name = "prithivMLmods/siglip2-x256p32-explicit-content"  # Replace with your HF model path if needed
 model = SiglipForImageClassification.from_pretrained(model_name)
 processor = AutoImageProcessor.from_pretrained(model_name)
 
@@ -88,7 +88,7 @@ iface = gr.Interface(
     fn=classify_explicit_content,
     inputs=gr.Image(type="numpy"),
     outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"),
-    title="siglip2-x384-explicit-content",
+    title= "siglip2-x256p32-explicit-content",
     description="Classifies images as Anime, Hentai, Pornography, Enticing, or Safe for use in moderation systems."
 )