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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Shoe-Net-10K |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-512 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- SigLIP2 |
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- Ballet Flat |
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- Boat |
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- Sneaker |
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- Clog |
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- Brogue |
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--- |
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# shoe-type-detection |
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> shoe-type-detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect different types of shoes such as **Ballet Flats**, **Boat Shoes**, **Brogues**, **Clogs**, and **Sneakers**. The model uses the `SiglipForImageClassification` architecture. |
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> \[!note] |
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> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features |
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> [https://arxiv.org/pdf/2502.14786](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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Ballet Flat 0.8980 0.9465 0.9216 2000 |
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Boat 0.9333 0.8750 0.9032 2000 |
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Brogue 0.9313 0.9490 0.9401 2000 |
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Clog 0.9244 0.8800 0.9016 2000 |
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Sneaker 0.9137 0.9480 0.9306 2000 |
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accuracy 0.9197 10000 |
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macro avg 0.9202 0.9197 0.9194 10000 |
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weighted avg 0.9202 0.9197 0.9194 10000 |
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``` |
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--- |
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## Label Space: 5 Classes |
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``` |
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Class 0: Ballet Flat |
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Class 1: Boat |
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Class 2: Brogue |
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Class 3: Clog |
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Class 4: Sneaker |
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``` |
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--- |
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## Install Dependencies |
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```bash |
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pip install -q transformers torch pillow gradio hf_xet |
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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/shoe-type-detection" # Update with actual model name on Hugging Face |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Updated label mapping |
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id2label = { |
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"0": "Ballet Flat", |
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"1": "Boat", |
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"2": "Brogue", |
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"3": "Clog", |
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"4": "Sneaker" |
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} |
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def classify_image(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 = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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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_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Shoe Type Classification"), |
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title="Shoe Type Detection", |
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description="Upload an image of a shoe to classify it as Ballet Flat, Boat, Brogue, Clog, or Sneaker." |
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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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`shoe-type-detection` is designed for: |
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* **E-Commerce Automation** – Automate product tagging and classification in online retail platforms. |
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* **Footwear Inventory Management** – Efficiently organize and categorize large volumes of shoe images. |
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* **Retail Intelligence** – Enable AI-powered search and filtering based on shoe types. |
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* **Smart Surveillance** – Identify and analyze footwear types in surveillance footage for retail analytics. |
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* **Fashion and Apparel Research** – Analyze trends in shoe types and customer preferences using image datasets. |
