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--- |
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license: apache-2.0 |
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datasets: |
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- deepghs/anime_classification |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Anime |
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- SigLIP2 |
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- Image-Detection |
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- 3D |
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- Comic |
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- Illustration |
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- Bangumi |
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--- |
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# **Anime-Classification-v1.0** |
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> **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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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3D 0.7979 0.8443 0.8204 4649 |
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Bangumi 0.8677 0.8728 0.8702 4914 |
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Comic 0.9716 0.9233 0.9468 5746 |
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Illustration 0.8204 0.8186 0.8195 6064 |
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accuracy 0.8648 21373 |
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macro avg 0.8644 0.8647 0.8642 21373 |
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weighted avg 0.8670 0.8648 0.8656 21373 |
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``` |
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--- |
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The model categorizes images into 4 anime-related classes: |
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``` |
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Class 0: "3D" |
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Class 1: "Bangumi" |
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Class 2: "Comic" |
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Class 3: "Illustration" |
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``` |
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--- |
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## **Install dependencies** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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--- |
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## **Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Anime-Classification-v1.0" # New model name |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def classify_anime_image(image): |
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"""Predicts the anime category for an input image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "3D", "1": "Bangumi", "2": "Comic", "3": "Illustration" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=classify_anime_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Anime Classification v1.0", |
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description="Upload an image to classify the anime style category." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Intended Use:** |
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The **Anime-Classification-v1.0** model is designed to classify anime-related images. Potential use cases include: |
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- **Content Tagging:** Automatically label anime artwork on platforms or apps. |
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- **Recommendation Engines:** Enhance personalized anime content suggestions. |
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- **Digital Art Curation:** Organize galleries by anime style for artists and fans. |
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- **Dataset Filtering:** Categorize and filter images during dataset creation. |
