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---
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
- Image-Classification
---
# **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.
> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
```py
Classification Report:
precision recall f1-score support
3D Printed 0.9108 0.9388 0.9246 25760
Not 3D Printed 0.9368 0.9081 0.9222 25760
accuracy 0.9234 51520
macro avg 0.9238 0.9234 0.9234 51520
weighted avg 0.9238 0.9234 0.9234 51520
```
---
## **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. |