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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- timm/mobilenetv3_large_100.ra_in1k |
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pipeline_tag: image-classification |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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A fine-tuned model for classifying invoices, credit card and bank cheque, trained using MobileNetV3_Large using Google Colab. |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Calvin |
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- **Model type:** Image Classification |
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- **License:** MIT |
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- **Finetuned from model:** timm/mobilenetv3_large_100.ra_in1k |
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''' |
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from tensorflow.keras.models import load_model |
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import cv2 |
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from tensorflow.keras.preprocessing.image import img_to_array |
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import tensorflow as tf |
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import numpy as np |
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from google.colab import files, drive |
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import matplotlib.pyplot as plt |
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# Define image size |
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IMG_SIZE = 224 |
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# Set confidence threshold |
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CONFIDENCE_THRESHOLD = 0.5 |
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drive.mount('/content/drive') |
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# Load the finetuned model |
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finetuned_model = load_model('/content/drive/MyDrive/models/finetuned_mobilenetv3large_model_v4.keras') |
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# Upload image using google.colab.files |
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uploaded = files.upload() |
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# Process the uploaded image |
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for fn in uploaded.keys(): |
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# Read the image |
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img_path = fn |
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img = cv2.imread(img_path) |
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# Resize the image (using tf.image.resize) |
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img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE]) |
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# Convert image to array |
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img_array = img_to_array(img) |
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# Normalize pixel values |
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img_array = img_array / 255.0 |
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# Add a dimension (batch size) |
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img_array = tf.expand_dims(img_array, 0) |
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# Perform inference using the finetuned model |
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prediction = finetuned_model.predict(img_array) |
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# Get probabilities for all classes |
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class_probabilities = prediction[0] |
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# Display results |
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print(f'{fn}:') |
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max_prob = np.max(class_probabilities) |
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if max_prob > CONFIDENCE_THRESHOLD: |
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for i, prob in enumerate(class_probabilities): |
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if i == 0: |
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class_name = 'Bank Cheque' |
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elif i == 1: |
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class_name = 'Credit Card' |
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elif i == 2: |
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class_name = 'Invoice' |
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else: |
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class_name = 'Others' |
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print(f' {class_name}: ({prob:.4f})') |
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else: |
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print('Others') |
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# Display the image |
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plt.imshow(img.numpy().astype(np.uint8)) |
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plt.title(fn) |
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plt.show() |
