|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- prithivMLmods/High_Res-vs-Low_Res |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- google/siglip2-base-patch16-224 |
|
|
pipeline_tag: image-classification |
|
|
library_name: transformers |
|
|
tags: |
|
|
- image |
|
|
- quality |
|
|
--- |
|
|
|
|
|
 |
|
|
|
|
|
# **High_Res-vs-Low_Res** |
|
|
|
|
|
> **High_Res-vs-Low_Res** 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 assess the resolution quality of images using the **SiglipForImageClassification** architecture. |
|
|
|
|
|
The model categorizes images into two classes: |
|
|
- **Class 0:** "High Resolution Image" – indicating that the image has a high resolution and appears sharp and detailed. |
|
|
- **Class 1:** "Low Resolution Image" – indicating that the image has a low resolution and may appear pixelated or blurry. |
|
|
|
|
|
```py |
|
|
Classification Report: |
|
|
precision recall f1-score support |
|
|
|
|
|
high resolution image 0.5697 0.5407 0.5548 1254 |
|
|
low resolution image 0.8495 0.8639 0.8566 3762 |
|
|
|
|
|
accuracy 0.7831 5016 |
|
|
macro avg 0.7096 0.7023 0.7057 5016 |
|
|
weighted avg 0.7795 0.7831 0.7812 5016 |
|
|
``` |
|
|
|
|
|
 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# **Run with Transformers🤗** |
|
|
|
|
|
```python |
|
|
!pip install -q transformers torch pillow gradio |
|
|
``` |
|
|
|
|
|
```python |
|
|
import gradio as gr |
|
|
from transformers import AutoImageProcessor |
|
|
from transformers import SiglipForImageClassification |
|
|
from transformers.image_utils import load_image |
|
|
from PIL import Image |
|
|
import torch |
|
|
|
|
|
# Load model and processor |
|
|
model_name = "prithivMLmods/High_Res-vs-Low_Res" |
|
|
model = SiglipForImageClassification.from_pretrained(model_name) |
|
|
processor = AutoImageProcessor.from_pretrained(model_name) |
|
|
|
|
|
def resolution_classification(image): |
|
|
"""Predicts image resolution classification.""" |
|
|
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": "High Resolution Image", "1": "Low Resolution Image"} |
|
|
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
|
|
|
|
|
return predictions |
|
|
|
|
|
# Create Gradio interface |
|
|
iface = gr.Interface( |
|
|
fn=resolution_classification, |
|
|
inputs=gr.Image(type="numpy"), |
|
|
outputs=gr.Label(label="Prediction Scores"), |
|
|
title="Image Resolution Classification", |
|
|
description="Upload an image to classify its resolution quality." |
|
|
) |
|
|
|
|
|
# Launch the app |
|
|
if __name__ == "__main__": |
|
|
iface.launch() |
|
|
``` |
|
|
|
|
|
# **Intended Use:** |
|
|
|
|
|
The **High_Res-vs-Low_Res** model is designed to evaluate the resolution quality of images. It helps distinguish between high-resolution and low-resolution images. Potential use cases include: |
|
|
|
|
|
- **Image Quality Assessment:** Identifying whether an image meets high-resolution standards or suffers from low-quality artifacts. |
|
|
- **Content Moderation:** Assisting platforms in filtering low-resolution images for better user experience. |
|
|
- **Forensic Analysis:** Supporting researchers and analysts in determining the clarity of images used in various applications. |
|
|
- **Image Processing Pipelines:** Helping developers optimize image enhancement algorithms by assessing resolution quality. |
