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---
license: apache-2.0
datasets:
- prithivMLmods/BnW-vs-Colored-10K
language:
- en
base_model:
- google/siglip2-so400m-patch16-512
pipeline_tag: image-classification
library_name: transformers
tags:
- B&W
- Colored
- art
- SigLIP2
---
# **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.
```py
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
```
---
The model categorizes images into 2 classes:
```
Class 0: "B & W"
Class 1: "Colored"
```
---
## **Install dependencies**
```python
!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/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()
```
---
## **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. |