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--- |
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tags: |
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- image-classification |
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- image |
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- data-classification |
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- image-categorisation |
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- data-categoriasation |
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pipeline_tag: image-classification |
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language: |
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- de |
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- en |
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--- |
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# Model Card for Model ID |
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This model is a Jewelry Classifier. Just upload an image of one of the categories named below and the model will classify it for you. |
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- Pendant |
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- Bracelet |
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- Chain |
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- Earring |
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- Ring |
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- Watch |
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# How to use? |
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Before following the steps below, please install these dependencies: |
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```pyhton |
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numpy==1.26.4 |
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keras==3.3.3 |
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pillow==10.3.0 |
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``` |
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### Step1: Load the Model (jewelry_classification.h5) |
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Download the model file from (https://huggingface.co/beyondxlabs/JewelryClassification/resolve/main/jewelry_classification.h5?download=true) and then use the below code snippet to load the model. |
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```python |
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model = load_model('jewelry_classification_model.h5') |
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class_labels = ['Anhänger', 'Armbänder', 'Ketten', 'Ohrringe', 'Ringe', 'Uhren'] |
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``` |
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### Step 2: Preprocess your images |
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Before giving images to the model, that image needs to be preprocessed to get a numpy array. You can just use the below function. |
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```python |
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def preprocess_image(img): |
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try: |
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img = Image.open(img) |
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img = img.resize((224, 224)) |
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img_array = img_to_array(img) |
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img_array = np.expand_dims(img_array, axis=0) |
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img_array = img_array.astype(np.float32) / 255.0 |
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return img_array |
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except Exception as error: |
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st.error(f"An error occurred during image preprocessing: {error}") |
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return None |
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``` |
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### Step 3: Predict the output |
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In this step the preprocessed image could be given to the model to get the classification. Below is the sample code snippet. |
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```python |
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def choose_category(img, is_url=True): |
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try: |
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processed_img = preprocess_image(img, is_url) |
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if processed_img is not None: |
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preds = model.predict(processed_img) |
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category = class_labels[np.argmax(preds)] |
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confidence = np.max(preds) |
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return category, confidence*100 |
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return 'Other', 0 |
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except Exception as e: |
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st.error(f"An error occurred during prediction: {e}") |
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return 'Other', 0 |
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``` |
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### Step 4(optional): Streamlit UI |
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Use the below snippet to make an UI Application using the model |
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```python |
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# UI interface |
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import streamlit as st |
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st.title("Jewelry Classification") |
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg"]) |
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if st.button("Classify"): |
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if uploaded_file is not None: |
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category, confidence = choose_category(uploaded_file, is_url=False) |
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st.write(f"Predicted Category: **{category}** with confidence **{confidence:.2f}%**") |
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else: |
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st.error("Please upload an image file.") |
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``` |
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