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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.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

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

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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

pip install -q transformers torch pillow gradio

Inference Code

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.

Base model