|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- ylecun/mnist |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- google/siglip2-base-patch16-224 |
|
|
pipeline_tag: image-classification |
|
|
library_name: transformers |
|
|
tags: |
|
|
- Digits |
|
|
- Mnist |
|
|
- SigLIP2 |
|
|
- 0-t0-9 |
|
|
- Number-Classification |
|
|
--- |
|
|
|
|
|
 |
|
|
 |
|
|
|
|
|
# **Mnist-Digits-SigLIP2** |
|
|
|
|
|
> **Mnist-Digits-SigLIP2** is an image classification model fine-tuned from **google/siglip2-base-patch16-224** to classify handwritten digits (0-9) using the **SiglipForImageClassification** architecture. It is trained on the MNIST dataset for accurate digit recognition. |
|
|
|
|
|
```py |
|
|
Classification Report: |
|
|
precision recall f1-score support |
|
|
|
|
|
0 0.9988 0.9959 0.9974 5923 |
|
|
1 0.9987 0.9918 0.9952 6742 |
|
|
2 0.9918 0.9943 0.9930 5958 |
|
|
3 0.9975 0.9938 0.9957 6131 |
|
|
4 0.9892 0.9882 0.9887 5842 |
|
|
5 0.9859 0.9937 0.9898 5421 |
|
|
6 0.9936 0.9939 0.9937 5918 |
|
|
7 0.9856 0.9943 0.9899 6265 |
|
|
8 0.9932 0.9921 0.9926 5851 |
|
|
9 0.9926 0.9897 0.9912 5949 |
|
|
|
|
|
accuracy 0.9928 60000 |
|
|
macro avg 0.9927 0.9928 0.9927 60000 |
|
|
weighted avg 0.9928 0.9928 0.9928 60000 |
|
|
``` |
|
|
|
|
|
 |
|
|
|
|
|
### **Classes:** |
|
|
- **Class 0:** "0" |
|
|
- **Class 1:** "1" |
|
|
- **Class 2:** "2" |
|
|
- **Class 3:** "3" |
|
|
- **Class 4:** "4" |
|
|
- **Class 5:** "5" |
|
|
- **Class 6:** "6" |
|
|
- **Class 7:** "7" |
|
|
- **Class 8:** "8" |
|
|
- **Class 9:** "9" |
|
|
|
|
|
--- |
|
|
|
|
|
# **Run with Transformers🤗** |
|
|
|
|
|
```python |
|
|
!pip install -q transformers torch pillow gradio |
|
|
``` |
|
|
|
|
|
```python |
|
|
import gradio as gr |
|
|
from transformers import AutoImageProcessor, SiglipForImageClassification |
|
|
from transformers.image_utils import load_image |
|
|
from PIL import Image |
|
|
import torch |
|
|
|
|
|
# Load model and processor |
|
|
model_name = "prithivMLmods/Mnist-Digits-SigLIP2" |
|
|
model = SiglipForImageClassification.from_pretrained(model_name) |
|
|
processor = AutoImageProcessor.from_pretrained(model_name) |
|
|
|
|
|
def classify_digit(image): |
|
|
"""Predicts the digit in the given handwritten digit 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() |
|
|
|
|
|
labels = { |
|
|
"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", |
|
|
"5": "5", "6": "6", "7": "7", "8": "8", "9": "9" |
|
|
} |
|
|
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
|
|
|
|
|
return predictions |
|
|
|
|
|
# Create Gradio interface |
|
|
iface = gr.Interface( |
|
|
fn=classify_digit, |
|
|
inputs=gr.Image(type="numpy"), |
|
|
outputs=gr.Label(label="Prediction Scores"), |
|
|
title="MNIST Digit Classification 🔢", |
|
|
description="Upload a handwritten digit image (0-9) to recognize it using MNIST-Digits-SigLIP2." |
|
|
) |
|
|
|
|
|
# Launch the app |
|
|
if __name__ == "__main__": |
|
|
iface.launch() |
|
|
``` |
|
|
|
|
|
--- |
|
|
|
|
|
# **Sample Inference** |
|
|
|
|
|
 |
|
|
 |
|
|
 |
|
|
 |
|
|
|
|
|
# **Intended Use:** |
|
|
|
|
|
The **Mnist-Digits-SigLIP2** model is designed for handwritten digit recognition. Potential applications include: |
|
|
|
|
|
- **Optical Character Recognition (OCR):** Digit recognition for various documents. |
|
|
- **Banking & Finance:** Automated check processing. |
|
|
- **Education & Learning:** AI-powered handwriting assessment. |
|
|
- **Embedded Systems:** Handwriting input in smart devices. |
