Update README.md

README.md

@@ -2,8 +2,22 @@
 license: apache-2.0
 datasets:
 - cmudrc/3d-printed-or-not
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- 3D-Printed-Or-Not
+- SigLIP2
 ---
 
+![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Z9102o2b66hGOm_ESlyOP.png)
+
+# **3D-Printed-Or-Not-SigLIP2**
+
+> **3D-Printed-Or-Not-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to distinguish between images of **3D printed** and **non-3D printed** objects using the **SiglipForImageClassification** architecture.
 
 ```py
 Classification Report:
@@ -19,3 +33,82 @@ Not 3D Printed     0.9368    0.9081    0.9222     25760
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/G1DWP3rDDJ_pO4SBlPbXR.png)
 
+
+---
+
+## **Label Space: 2 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+Class 0: "3D Printed"
+Class 1: "Not 3D Printed"
+```
+
+---
+
+## **Install Dependencies**
+
+```bash
+pip install -q transformers torch 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/3D-Printed-Or-Not-SigLIP2"  # Replace with your model path if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "3D Printed",
+    "1": "Not 3D Printed"
+}
+
+def classify_3d_printed(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_3d_printed,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="3D Printing Classification"),
+    title="3D-Printed-Or-Not-SigLIP2",
+    description="Upload an image to detect if the object is 3D printed or not."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**3D-Printed-Or-Not-SigLIP2** can be used for:
+
+- **Manufacturing Verification** – Classify objects to ensure they meet production standards.
+- **Educational Tools** – Train models and learners to distinguish between manufacturing methods.
+- **Retail Filtering** – Categorize product images by manufacturing technique.
+- **Quality Control** – Spot check datasets or content for 3D printing.