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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Realistic-Portrait-Gender-1024px |
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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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- Gender |
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- Classification |
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- art |
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- realism |
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- portrait |
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- Male |
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- Female |
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- SigLIP2 |
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--- |
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# **Realistic-Gender-Classification** |
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> **Realistic-Gender-Classification** is a binary image classification model based on `google/siglip2-base-patch16-224`, designed to classify **gender** from realistic human portrait images. It can be used in **demographic analysis**, **personalization systems**, and **automated tagging** in large-scale image datasets. |
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> [!note] |
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*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786 |
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```py |
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Classification Report: |
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precision recall f1-score support |
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female portrait 0.9754 0.9656 0.9705 1600 |
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male portrait 0.9660 0.9756 0.9708 1600 |
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accuracy 0.9706 3200 |
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macro avg 0.9707 0.9706 0.9706 3200 |
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weighted avg 0.9707 0.9706 0.9706 3200 |
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``` |
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--- |
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## **Label Classes** |
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The model distinguishes between the following portrait gender categories: |
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``` |
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0: female portrait |
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1: male portrait |
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``` |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torch pillow gradio |
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``` |
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--- |
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## **Example 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/Realistic-Gender-Classification" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# ID to label mapping |
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id2label = { |
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"0": "female portrait", |
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"1": "male portrait" |
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} |
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def classify_gender(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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prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_gender, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=2, label="Gender Classification"), |
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title="Realistic-Gender-Classification", |
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description="Upload a realistic portrait image to classify it as 'female portrait' or 'male portrait'." |
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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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## Demo Inference |
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> [!note] |
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female portrait |
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> [!note] |
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male portrait |
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## **Applications** |
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* **Demographic Insights in Visual Data** |
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* **Dataset Curation & Tagging** |
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* **Media Analytics** |
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* **Audience Profiling for Marketing** |
