Update README.md

README.md

@@ -32,7 +32,9 @@ Converted to TFLite Float32 & Float16 formats by Youssef Boulaouane.
 ## Model Usage
 ### Image Classification in Python
 ```python
+import numpy as np
 import tensorflow as tf
+from PIL import Image
 
 # Load label file
 with open('imagenet_classes.txt', 'r') as file:
@@ -47,16 +49,15 @@ input_details = tfl_model.get_input_details()
 output_details = tfl_model.get_output_details()
 
 # Load and preprocess the image
-mean = [0.485, 0.456, 0.406]
-std = [0.229, 0.224, 0.225]
+image = Image.open(image_path).resize((224, 224), Image.BICUBIC)
 
-image = tf.image.resize(
-    tf.image.decode_image(tf.io.read_file(image_path), channels=3),
-    [256, 256],
-    method="bicubic",
-)
-image = tf.image.central_crop(image, central_fraction=224.0/256.0)
-image = (image - mean) / std
+image = np.array(image, dtype=np.float32)
+mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+image = (image / 255.0 - mean) / std
+
+image = np.expand_dims(image, axis=-1)
+image = np.rollaxis(image, 3)
 
 # Inference and postprocessing
 input = input_details[0]
@@ -68,6 +69,13 @@ tfl_output_tensor = tf.convert_to_tensor(tfl_output)
 tfl_softmax_output = tf.nn.softmax(tfl_output_tensor, axis=1)
 
 tfl_top5_probs, tfl_top5_indices = tf.math.top_k(tfl_softmax_output, k=5)
+
+# Get the top5 class labels and probabilities
+tfl_probs_list = tfl_top5_probs[0].numpy().tolist()
+tfl_index_list = tfl_top5_indices[0].numpy().tolist()
+
+for index, prob in zip(tfl_index_list, tfl_probs_list):
+    print(f"{index_to_label[index]}: {round(prob*100, 2)}%")
 ```
 
 ### Deployment on Mobile