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