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Anime-Classification-v1.0

Anime-Classification-v1.0 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 anime-related images using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 3D 0.7979 0.8443 0.8204 4649
 Bangumi 0.8677 0.8728 0.8702 4914
 Comic 0.9716 0.9233 0.9468 5746
Illustration 0.8204 0.8186 0.8195 6064

 accuracy 0.8648 21373
 macro avg 0.8644 0.8647 0.8642 21373
weighted avg 0.8670 0.8648 0.8656 21373

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The model categorizes images into 4 anime-related classes:

 Class 0: "3D"
 Class 1: "Bangumi"
 Class 2: "Comic"
 Class 3: "Illustration"

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Install dependencies

!pip install -q transformers torch pillow gradio

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Anime-Classification-v1.0" # New model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def classify_anime_image(image):
 """Predicts the anime category for an input 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": "3D", "1": "Bangumi", "2": "Comic", "3": "Illustration"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=classify_anime_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Anime Classification v1.0",
 description="Upload an image to classify the anime style category."
)

if __name__ == "__main__":
 iface.launch()

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Intended Use:

The Anime-Classification-v1.0 model is designed to classify anime-related images. Potential use cases include:

• Content Tagging: Automatically label anime artwork on platforms or apps.
• Recommendation Engines: Enhance personalized anime content suggestions.
• Digital Art Curation: Organize galleries by anime style for artists and fans.
• Dataset Filtering: Categorize and filter images during dataset creation.

Base model

google/siglip2-base-patch16-224

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