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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-so400m-patch14-384 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- fashion |
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- product |
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- usage |
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- Casual |
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- Ethnic |
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- Formal |
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--- |
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# **Fashion-Product-Usage** |
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> **Fashion-Product-Usage** is a vision-language model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images based on their intended usage context. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Casual 0.8529 0.9716 0.9084 34392 |
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Ethnic 0.8365 0.7528 0.7925 3208 |
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Formal 0.7246 0.3006 0.4250 2345 |
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Home 0.0000 0.0000 0.0000 1 |
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Party 0.0000 0.0000 0.0000 29 |
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Smart Casual 0.0000 0.0000 0.0000 67 |
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Sports 0.7157 0.1848 0.2938 4004 |
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Travel 0.0000 0.0000 0.0000 26 |
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accuracy 0.8458 44072 |
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macro avg 0.3912 0.2762 0.3024 44072 |
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weighted avg 0.8300 0.8458 0.8159 44072 |
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``` |
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The model predicts one of the following usage categories: |
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- **0:** Casual |
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- **1:** Ethnic |
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- **2:** Formal |
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- **3:** Home |
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- **4:** Party |
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- **5:** Smart Casual |
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- **6:** Sports |
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- **7:** Travel |
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--- |
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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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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/Fashion-Product-Usage" # Replace with your actual 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: "Casual", |
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1: "Ethnic", |
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2: "Formal", |
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3: "Home", |
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4: "Party", |
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5: "Smart Casual", |
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6: "Sports", |
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7: "Travel" |
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} |
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def classify_usage(image): |
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"""Predicts the usage type of a fashion product.""" |
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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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return predictions |
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# Gradio interface |
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iface = gr.Interface( |
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fn=classify_usage, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Usage Prediction Scores"), |
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title="Fashion-Product-Usage", |
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description="Upload a fashion product image to predict its intended usage (Casual, Formal, Party, etc.)." |
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) |
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# Launch the app |
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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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This model can be used for: |
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- **Product tagging in e-commerce catalogs** |
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- **Context-aware product recommendations** |
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- **Fashion search optimization** |
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- **Data annotation for training recommendation engines** |
