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BnW-vs-Colored-Detection

BnW-vs-Colored-Detection is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish between black & white and colored images using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 B & W 0.9982 0.9996 0.9989 5000
 Colored 0.9996 0.9982 0.9989 5000

 accuracy 0.9989 10000
 macro avg 0.9989 0.9989 0.9989 10000
weighted avg 0.9989 0.9989 0.9989 10000

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The model categorizes images into 2 classes:

 Class 0: "B & W"
 Class 1: "Colored"

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Install dependencies

!pip install -q transformers torch pillow gradio

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/BnW-vs-Colored-Detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def classify_bw_colored(image):
 """Predicts if an image is Black & White or Colored."""
 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()
 
 labels = {
 "0": "B & W", "1": "Colored"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=classify_bw_colored,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="BnW vs Colored Detection",
 description="Upload an image to detect if it is Black & White or Colored."
)

if __name__ == "__main__":
 iface.launch()

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Intended Use:

The BnW-vs-Colored-Detection model is designed to classify images by color mode. Potential use cases include:

• Archive Organization: Separate historical B&W images from modern colored ones.
• Data Filtering: Preprocess image datasets by removing or labeling specific types.
• Digital Restoration: Assist in determining candidates for colorization.
• Search & Categorization: Enable efficient tagging and filtering in image libraries.

Base model

google/siglip2-so400m-patch16-512

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