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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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base_model: |
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- facebook/metaclip-2-worldwide-s16 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- gender-identifier |
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--- |
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# **MetaCLIP-2-Gender-Identifier** |
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> **MetaCLIP-2-Gender-Identifier** is an image classification vision-language encoder model fine-tuned from **[facebook/metaclip-2-worldwide-s16](https://huggingface.co/facebook/metaclip-2-worldwide-s16)** for a single-label classification task. |
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> It is designed to predict the gender of a person from an image using the **MetaClip2ForImageClassification** architecture. |
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>[!note] |
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MetaCLIP 2: A Worldwide Scaling Recipe : https://huggingface.co/papers/2507.22062 |
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``` |
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Classification Report: |
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precision recall f1-score support |
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female 0.9815 0.9631 0.9722 1600 |
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male 0.9638 0.9819 0.9728 1600 |
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accuracy 0.9725 3200 |
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macro avg 0.9727 0.9725 0.9725 3200 |
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weighted avg 0.9727 0.9725 0.9725 3200 |
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``` |
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--- |
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The model categorizes images into two gender classes: |
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* **Class 0:** "female" |
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* **Class 1:** "male" |
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# **Run with Transformers** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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import torch |
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from transformers import AutoImageProcessor, AutoModelForImageClassification |
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from PIL import Image |
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# Model name from Hugging Face Hub |
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model_name = "prithivMLmods/MetaCLIP-2-Gender-Identifier" |
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# Load processor and model |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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model = AutoModelForImageClassification.from_pretrained(model_name) |
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model.eval() |
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# Define labels |
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LABELS = { |
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0: "female", |
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1: "male" |
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} |
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def age_classification(image): |
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"""Predict the age group of a person 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 = {LABELS[i]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Build Gradio interface |
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iface = gr.Interface( |
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fn=age_classification, |
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inputs=gr.Image(type="numpy", label="Upload Image"), |
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outputs=gr.Label(label="Predicted Gender"), |
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title="MetaCLIP-2-Gender-Identifier", |
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description="Upload an image to predict the person's gender." |
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) |
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# Launch app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Sample Inference:** |
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# **Intended Use:** |
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The **MetaCLIP-2-Gender-Identifier** model is designed to classify images into gender categories. |
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Potential use cases include: |
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* **Demographic Analysis:** Supporting research and business insights into gender-based distribution. |
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* **Health and Fitness Applications:** Assisting in gender-specific analytics and recommendations. |
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* **Security and Access Control:** Supporting gender-based identity verification systems. |
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* **Retail and Marketing:** Enabling improved personalization and customer segmentation. |
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* **Forensics and Surveillance:** Assisting in identity estimation for investigative purposes. |
