beyondxlabs
/

ImageClassification

@@ -6,194 +6,86 @@ tags:
 - image-categorisation
 - data-categoriasation
 pipeline_tag: image-classification
 ---
 # Model Card for Model ID
-This model is designed for efficient image classification. Simply upload a picture, and the model will automatically categorize it into one of the predefined groups. It is suitable for a wide range of applications, including object recognition, scene analysis, and more. The model leverages advanced deep learning techniques to ensure high accuracy and fast processing. Perfect for both developers and researchers looking to streamline their image classification tasks.
-### Model Description
-<!-- . -->
-- **Developed by:** [BXL Digital]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 - 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 (link) 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.")
+```