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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"

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

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Run with Transformers🤗

!pip install -q transformers torch pillow gradio

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.

Base model

google/siglip2-base-patch16-224

(120)

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