├── requirements.txt ├── labels.txt ├── app.py ├── .gitattributes ├── README.md └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | transformers 2 | torch 3 | pillow 4 | gradio 5 | -------------------------------------------------------------------------------- /labels.txt: -------------------------------------------------------------------------------- 1 | # Label mapping 2 | id2label = { 3 | 0: "Casual", 4 | 1: "Ethnic", 5 | 2: "Formal", 6 | 3: "Home", 7 | 4: "Party", 8 | 5: "Smart Casual", 9 | 6: "Sports", 10 | 7: "Travel" 11 | } 12 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | from transformers import AutoImageProcessor, SiglipForImageClassification 3 | from PIL import Image 4 | import torch 5 | 6 | # Load model and processor 7 | model_name = "prithivMLmods/Fashion-Product-Usage" # Replace with your actual model path 8 | model = SiglipForImageClassification.from_pretrained(model_name) 9 | processor = AutoImageProcessor.from_pretrained(model_name) 10 | 11 | # Label mapping 12 | id2label = { 13 | 0: "Casual", 14 | 1: "Ethnic", 15 | 2: "Formal", 16 | 3: "Home", 17 | 4: "Party", 18 | 5: "Smart Casual", 19 | 6: "Sports", 20 | 7: "Travel" 21 | } 22 | 23 | def classify_usage(image): 24 | """Predicts the usage type of a fashion product.""" 25 | image = Image.fromarray(image).convert("RGB") 26 | inputs = processor(images=image, return_tensors="pt") 27 | 28 | with torch.no_grad(): 29 | outputs = model(**inputs) 30 | logits = outputs.logits 31 | probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() 32 | 33 | predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))} 34 | return predictions 35 | 36 | # Gradio interface 37 | iface = gr.Interface( 38 | fn=classify_usage, 39 | inputs=gr.Image(type="numpy"), 40 | outputs=gr.Label(label="Usage Prediction Scores"), 41 | title="Fashion-Product-Usage", 42 | description="Upload a fashion product image to predict its intended usage (Casual, Formal, Party, etc.)." 43 | ) 44 | 45 | # Launch the app 46 | if __name__ == "__main__": 47 | iface.launch() 48 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | *.7z filter=lfs diff=lfs merge=lfs -text 2 | *.arrow filter=lfs diff=lfs merge=lfs -text 3 | *.bin filter=lfs diff=lfs merge=lfs -text 4 | *.bz2 filter=lfs diff=lfs merge=lfs -text 5 | *.ckpt filter=lfs diff=lfs merge=lfs -text 6 | *.ftz filter=lfs diff=lfs merge=lfs -text 7 | *.gz filter=lfs diff=lfs merge=lfs -text 8 | *.h5 filter=lfs diff=lfs merge=lfs -text 9 | *.joblib filter=lfs diff=lfs merge=lfs -text 10 | *.lfs.* filter=lfs diff=lfs merge=lfs -text 11 | *.mlmodel filter=lfs diff=lfs merge=lfs -text 12 | *.model filter=lfs diff=lfs merge=lfs -text 13 | *.msgpack filter=lfs diff=lfs merge=lfs -text 14 | *.npy filter=lfs diff=lfs merge=lfs -text 15 | *.npz filter=lfs diff=lfs merge=lfs -text 16 | *.onnx filter=lfs diff=lfs merge=lfs -text 17 | *.ot filter=lfs diff=lfs merge=lfs -text 18 | *.parquet filter=lfs diff=lfs merge=lfs -text 19 | *.pb filter=lfs diff=lfs merge=lfs -text 20 | *.pickle filter=lfs diff=lfs merge=lfs -text 21 | *.pkl filter=lfs diff=lfs merge=lfs -text 22 | *.pt filter=lfs diff=lfs merge=lfs -text 23 | *.pth filter=lfs diff=lfs merge=lfs -text 24 | *.rar filter=lfs diff=lfs merge=lfs -text 25 | *.safetensors filter=lfs diff=lfs merge=lfs -text 26 | saved_model/**/* filter=lfs diff=lfs merge=lfs -text 27 | *.tar.* filter=lfs diff=lfs merge=lfs -text 28 | *.tar filter=lfs diff=lfs merge=lfs -text 29 | *.tflite filter=lfs diff=lfs merge=lfs -text 30 | *.tgz filter=lfs diff=lfs merge=lfs -text 31 | *.wasm filter=lfs diff=lfs merge=lfs -text 32 | *.xz filter=lfs diff=lfs merge=lfs -text 33 | *.zip filter=lfs diff=lfs merge=lfs -text 34 | *.zst filter=lfs diff=lfs merge=lfs -text 35 | *tfevents* filter=lfs diff=lfs merge=lfs -text 36 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ![14.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LNIOy8V_w0loMrVMtTyIK.png) 4 | 5 | # **Fashion-Product-Usage** 6 | 7 | > **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. 8 | 9 | ```py 10 | Classification Report: 11 | precision recall f1-score support 12 | 13 | Casual 0.8529 0.9716 0.9084 34392 14 | Ethnic 0.8365 0.7528 0.7925 3208 15 | Formal 0.7246 0.3006 0.4250 2345 16 | Home 0.0000 0.0000 0.0000 1 17 | Party 0.0000 0.0000 0.0000 29 18 | Smart Casual 0.0000 0.0000 0.0000 67 19 | Sports 0.7157 0.1848 0.2938 4004 20 | Travel 0.0000 0.0000 0.0000 26 21 | 22 | accuracy 0.8458 44072 23 | macro avg 0.3912 0.2762 0.3024 44072 24 | weighted avg 0.8300 0.8458 0.8159 44072 25 | ``` 26 | 27 | The model predicts one of the following usage categories: 28 | 29 | - **0:** Casual 30 | - **1:** Ethnic 31 | - **2:** Formal 32 | - **3:** Home 33 | - **4:** Party 34 | - **5:** Smart Casual 35 | - **6:** Sports 36 | - **7:** Travel 37 | 38 | --- 39 | 40 | # **Run with Transformers 🤗** 41 | 42 | ```python 43 | !pip install -q transformers torch pillow gradio 44 | ``` 45 | 46 | ```python 47 | import gradio as gr 48 | from transformers import AutoImageProcessor, SiglipForImageClassification 49 | from PIL import Image 50 | import torch 51 | 52 | # Load model and processor 53 | model_name = "prithivMLmods/Fashion-Product-Usage" # Replace with your actual model path 54 | model = SiglipForImageClassification.from_pretrained(model_name) 55 | processor = AutoImageProcessor.from_pretrained(model_name) 56 | 57 | # Label mapping 58 | id2label = { 59 | 0: "Casual", 60 | 1: "Ethnic", 61 | 2: "Formal", 62 | 3: "Home", 63 | 4: "Party", 64 | 5: "Smart Casual", 65 | 6: "Sports", 66 | 7: "Travel" 67 | } 68 | 69 | def classify_usage(image): 70 | """Predicts the usage type of a fashion product.""" 71 | image = Image.fromarray(image).convert("RGB") 72 | inputs = processor(images=image, return_tensors="pt") 73 | 74 | with torch.no_grad(): 75 | outputs = model(**inputs) 76 | logits = outputs.logits 77 | probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() 78 | 79 | predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))} 80 | return predictions 81 | 82 | # Gradio interface 83 | iface = gr.Interface( 84 | fn=classify_usage, 85 | inputs=gr.Image(type="numpy"), 86 | outputs=gr.Label(label="Usage Prediction Scores"), 87 | title="Fashion-Product-Usage", 88 | description="Upload a fashion product image to predict its intended usage (Casual, Formal, Party, etc.)." 89 | ) 90 | 91 | # Launch the app 92 | if __name__ == "__main__": 93 | iface.launch() 94 | ``` 95 | 96 | --- 97 | 98 | # **Intended Use** 99 | 100 | This model can be used for: 101 | 102 | - **Product tagging in e-commerce catalogs** 103 | - **Context-aware product recommendations** 104 | - **Fashion search optimization** 105 | - **Data annotation for training recommendation engines** 106 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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