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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Multilabel-Portrait-18K |
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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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--- |
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# **Multilabel-Portrait-SigLIP2** |
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> **Multilabel-Portrait-SigLIP2** is a vision-language model fine-tuned from [**google/siglip2-base-patch16-224**](https://huggingface.co/google/siglip2-base-patch16-224) using the `SiglipForImageClassification` architecture. It classifies portrait-style images into one of the following **visual portrait categories**: |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Anime Portrait 0.9989 0.9991 0.9990 4444 |
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Cartoon Portrait 0.9964 0.9926 0.9945 4444 |
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Real Portrait 0.9964 0.9971 0.9967 4444 |
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Sketch Portrait 0.9971 1.0000 0.9985 4444 |
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accuracy 0.9972 17776 |
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macro avg 0.9972 0.9972 0.9972 17776 |
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weighted avg 0.9972 0.9972 0.9972 17776 |
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``` |
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--- |
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# **Model Objective** |
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The model is designed to **analyze portrait images** and categorize them into **one of four distinct portrait types**: |
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- **0:** Anime Portrait |
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- **1:** Cartoon Portrait |
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- **2:** Real Portrait |
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- **3:** Sketch Portrait |
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--- |
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# **Try it with Transformers 🤗** |
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Install dependencies: |
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```bash |
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pip install -q transformers torch pillow gradio |
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``` |
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Run the model with the following script: |
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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/Multilabel-Portrait-SigLIP2" # Replace with actual HF model path |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Label mapping |
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id2label = { |
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0: "Anime Portrait", |
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1: "Cartoon Portrait", |
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2: "Real Portrait", |
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3: "Sketch Portrait" |
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} |
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def classify_portrait(image): |
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"""Predict the type of portrait style from an 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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predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))} |
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predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True)) |
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return predictions |
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# Gradio interface |
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iface = gr.Interface( |
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fn=classify_portrait, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Portrait Type Prediction Scores"), |
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title="Multilabel-Portrait-SigLIP2", |
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description="Upload a portrait-style image (anime, cartoon, real, or sketch) to predict its most likely visual 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 Cases** |
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- **AI Art Curation** — Automatically organize large-scale datasets of artistic portraits. |
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- **Style-based Portrait Analysis** — Determine artistic style in user-uploaded or curated portrait datasets. |
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- **Content Filtering for Platforms** — Group and recommend based on visual aesthetics. |
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- **Dataset Pre-labeling** — Helps reduce manual effort in annotation tasks. |
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- **User Avatar Classification** — Profile categorization in social or gaming platforms. |
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