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---
license: apache-2.0
datasets:
- Siraitia/deeplearning-catdog
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Cat
- Dog
- Classification
- SigLIP2
- Vision-encoder
---
# **PussyCat-vs-Doggie-SigLIP2**
> **PussyCat-vs-Doggie-SigLIP2** 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 classify images as either a cat or a dog using the **SiglipForImageClassification** architecture.
The model categorizes images into two classes:
- **Class 0:** "Pussy Cat"
- **Class 1:** "Doggie"
```py
Classification Report:
precision recall f1-score support
Pussy Cat 0.9194 0.8745 0.8964 12500
Doggie 0.8803 0.9234 0.9013 12500
accuracy 0.8989 25000
macro avg 0.8999 0.8989 0.8989 25000
weighted avg 0.8999 0.8989 0.8989 25000
```
# **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/PussyCat-vs-Doggie-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def animal_classification(image):
"""Predicts whether the image contains a cat or a dog."""
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": "Pussy Cat",
"1": "Doggie"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=animal_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Cat vs Dog Classification",
description="Upload an image to classify whether it contains a cat or a dog."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **PussyCat-vs-Doggie-SigLIP2** model is designed to classify images as either a cat or a dog. Potential use cases include:
- **Pet Identification:** Helping users distinguish between cats and dogs.
- **Automated Pet Sorting:** Useful for shelters and pet adoption platforms.
- **Educational Purposes:** Assisting in teaching image classification concepts.
- **Surveillance & Security:** Identifying animals in security footage. |