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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-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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- color |
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- cloth |
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--- |
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# **Fashion-Product-baseColour** |
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> **Fashion-Product-baseColour** is a visual classification model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It predicts the **base color** of fashion products from images — enabling accurate tagging, search, and recommendation in fashion-related applications. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Beige 0.4338 0.5409 0.4815 745 |
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Black 0.8051 0.8656 0.8342 9699 |
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Blue 0.7513 0.7858 0.7682 4906 |
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Bronze 0.0000 0.0000 0.0000 89 |
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Brown 0.6812 0.7596 0.7183 3440 |
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Burgundy 0.0000 0.0000 0.0000 44 |
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Charcoal 0.4941 0.1842 0.2684 228 |
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Coffee Brown 0.0000 0.0000 0.0000 29 |
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Copper 0.5000 0.0120 0.0235 83 |
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Cream 0.3940 0.3446 0.3677 383 |
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Fluorescent Green 0.0000 0.0000 0.0000 5 |
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Gold 0.4935 0.6747 0.5701 621 |
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Green 0.7286 0.7760 0.7516 2103 |
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Grey 0.6313 0.5002 0.5581 2735 |
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Grey Melange 0.5728 0.4041 0.4739 146 |
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Khaki 0.3540 0.2878 0.3175 139 |
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Lavender 0.5049 0.3250 0.3954 160 |
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Lime Green 0.0000 0.0000 0.0000 5 |
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Magenta 0.5909 0.1016 0.1733 128 |
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Maroon 0.5121 0.2929 0.3727 577 |
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Mauve 0.0000 0.0000 0.0000 28 |
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Metallic 0.0000 0.0000 0.0000 41 |
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Multi 0.4005 0.3832 0.3917 394 |
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Mushroom Brown 0.0000 0.0000 0.0000 16 |
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Mustard 0.4912 0.2887 0.3636 97 |
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Navy Blue 0.6290 0.4905 0.5512 1784 |
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Nude 0.0000 0.0000 0.0000 21 |
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Off White 0.5789 0.2418 0.3411 182 |
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Olive 0.5259 0.5208 0.5233 409 |
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Orange 0.6838 0.6119 0.6458 523 |
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Peach 0.4727 0.4216 0.4457 185 |
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Pink 0.6912 0.7423 0.7158 1824 |
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Purple 0.6846 0.7568 0.7189 1612 |
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Red 0.6916 0.8273 0.7534 2432 |
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Rose 0.0000 0.0000 0.0000 21 |
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Rust 0.5000 0.1692 0.2529 65 |
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Sea Green 0.0000 0.0000 0.0000 22 |
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Silver 0.6088 0.4830 0.5387 1089 |
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Skin 0.5479 0.6319 0.5869 163 |
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Steel 0.2857 0.0381 0.0672 315 |
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Tan 0.6667 0.0357 0.0678 112 |
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Taupe 0.0000 0.0000 0.0000 11 |
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Teal 0.4857 0.2857 0.3598 119 |
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Turquoise Blue 0.0000 0.0000 0.0000 69 |
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White 0.7518 0.7950 0.7728 5497 |
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Yellow 0.7714 0.8003 0.7856 776 |
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accuracy 0.7072 44072 |
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macro avg 0.4112 0.3343 0.3469 44072 |
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weighted avg 0.6919 0.7072 0.6935 44072 |
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``` |
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The model categorizes fashion product images into the following **46 base color classes**: |
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- Beige, Black, Blue, Bronze, Brown, Burgundy, Charcoal, Coffee Brown, Copper, Cream |
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- Fluorescent Green, Gold, Green, Grey, Grey Melange, Khaki, Lavender, Lime Green |
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- Magenta, Maroon, Mauve, Metallic, Multi, Mushroom Brown, Mustard, Navy Blue |
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- Nude, Off White, Olive, Orange, Peach, Pink, Purple, Red, Rose, Rust |
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- Sea Green, Silver, Skin, Steel, Tan, Taupe, Teal, Turquoise Blue, White, Yellow |
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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-baseColour" # Replace with 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: "Beige", 1: "Black", 2: "Blue", 3: "Bronze", 4: "Brown", 5: "Burgundy", |
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6: "Charcoal", 7: "Coffee Brown", 8: "Copper", 9: "Cream", 10: "Fluorescent Green", |
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11: "Gold", 12: "Green", 13: "Grey", 14: "Grey Melange", 15: "Khaki", 16: "Lavender", |
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17: "Lime Green", 18: "Magenta", 19: "Maroon", 20: "Mauve", 21: "Metallic", |
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22: "Multi", 23: "Mushroom Brown", 24: "Mustard", 25: "Navy Blue", 26: "Nude", |
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27: "Off White", 28: "Olive", 29: "Orange", 30: "Peach", 31: "Pink", 32: "Purple", |
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33: "Red", 34: "Rose", 35: "Rust", 36: "Sea Green", 37: "Silver", 38: "Skin", |
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39: "Steel", 40: "Tan", 41: "Taupe", 42: "Teal", 43: "Turquoise Blue", 44: "White", 45: "Yellow" |
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} |
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def classify_base_color(image): |
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"""Predicts the base color of a fashion product 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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return predictions |
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# Gradio interface |
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iface = gr.Interface( |
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fn=classify_base_color, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Base Colour Prediction Scores"), |
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title="Fashion-Product-baseColour", |
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description="Upload a fashion product image to detect its primary color (e.g., Red, Black, Cream, Navy Blue, 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 is ideal for: |
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- **E-commerce platforms** for accurate product color labeling |
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- **Fashion search engines** and recommendation systems |
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- **Inventory and catalog automation** |
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- **Fashion analytics and trends tracking** |
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- **Design tools** for color-based sorting and filters |
