Update README.md

README.md

@@ -7,4 +7,88 @@ metrics:
 base_model:
 - timm/mobilenetv3_large_100.ra_in1k
 pipeline_tag: image-classification
----
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+A fine-tuned model for classifying invoices, credit card and bank cheque, trained using MobileNetV3_Large using Google Colab.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Calvin
+- **Model type:** Image Classification
+- **License:** MIT
+- **Finetuned from model:** timm/mobilenetv3_large_100.ra_in1k
+
+'''
+  from tensorflow.keras.models import load_model
+  import cv2
+  from tensorflow.keras.preprocessing.image import img_to_array
+  import tensorflow as tf
+  import numpy as np
+  from google.colab import files, drive
+  import matplotlib.pyplot as plt
+  
+  # Define image size
+  IMG_SIZE = 224
+  
+  # Set confidence threshold
+  CONFIDENCE_THRESHOLD = 0.5
+  
+  drive.mount('/content/drive')
+  
+  # Load the finetuned model
+  finetuned_model = load_model('/content/drive/MyDrive/models/finetuned_mobilenetv3large_model_v4.keras')
+  
+  # Upload image using google.colab.files
+  uploaded = files.upload()
+  
+  # Process the uploaded image
+  for fn in uploaded.keys():
+      # Read the image
+      img_path = fn
+      img = cv2.imread(img_path)
+  
+      # Resize the image (using tf.image.resize)
+      img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE])
+  
+      # Convert image to array
+      img_array = img_to_array(img)
+  
+      # Normalize pixel values
+      img_array = img_array / 255.0
+  
+      # Add a dimension (batch size)
+      img_array = tf.expand_dims(img_array, 0)
+  
+      # Perform inference using the finetuned model
+      prediction = finetuned_model.predict(img_array)
+  
+      # Get probabilities for all classes
+      class_probabilities = prediction[0]
+  
+      # Display results
+      print(f'{fn}:')
+  
+      max_prob = np.max(class_probabilities)
+      if max_prob > CONFIDENCE_THRESHOLD:
+          for i, prob in enumerate(class_probabilities):
+              if i == 0:
+                  class_name = 'Bank Cheque'
+              elif i == 1:
+                  class_name = 'Credit Card'
+              elif i == 2:
+                  class_name = 'Invoice'
+              else:
+                  class_name = 'Others'
+              print(f' {class_name}: ({prob:.4f})')
+      else:
+          print('Others')
+  
+      # Display the image
+      plt.imshow(img.numpy().astype(np.uint8))
+      plt.title(fn)
+      plt.show()