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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+CatClassifier.keras filter=lfs diff=lfs merge=lfs -text

CatBreedClassifier.py

@@ -0,0 +1,107 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models
+import random
+
+# Dataset used for training is a combination of the following datasets:
+# Oxford IIIT Cats: https://www.kaggle.com/datasets/imbikramsaha/cat-breeds
+# CatBreedsRefined-7k: https://www.kaggle.com/datasets/doctrinek/catbreedsrefined-7k
+
+images_path = "CatsDataset"
+batch_size = 32
+img_size = 400 # Images will be converted to 400 x 400 before input into the model
+seed = random.randrange(1, 100) # Or set manually
+test_split = 0.2
+
+print("Seed:", seed)
+
+# Load dataset and split into test and train sets
+train_set = tf.keras.utils.image_dataset_from_directory(
+    images_path,
+    validation_split=test_split,
+    subset="training",
+    seed=seed,
+    image_size=(img_size, img_size),
+    batch_size=batch_size
+)
+
+test_set = tf.keras.utils.image_dataset_from_directory(
+  images_path,
+  validation_split=test_split,
+  subset="validation",
+  seed=seed,
+  image_size=(img_size, img_size),
+  batch_size=batch_size
+)
+
+# Cache dataset to disk
+train_set = train_set.cache().prefetch(buffer_size=tf.data.AUTOTUNE)
+test_set = test_set.cache().prefetch(buffer_size=tf.data.AUTOTUNE)
+
+num_classes = 12
+
+# CNN Model
+model = models.Sequential()
+model.add(layers.RandomFlip("horizontal_and_vertical"))
+model.add(layers.RandomRotation(0.2))
+model.add(layers.RandomZoom((0, 0.2)))
+model.add(layers.Rescaling(1./255))
+
+model.add(layers.Conv2D(8, 3, activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D())
+
+model.add(layers.Conv2D(16, 3, activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D())
+
+model.add(layers.Conv2D(32, 3, activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D())
+
+model.add(layers.Conv2D(64, 3, activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D())
+
+model.add(layers.Conv2D(92, 3, activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D())
+
+model.add(layers.BatchNormalization())
+
+model.add(layers.Flatten())
+
+model.add(layers.Dense(1024, activation="relu"))
+model.add(layers.Dropout(0.5))
+model.add(layers.Dense(512, activation="relu"))
+model.add(layers.Dense(num_classes, activation="softmax"))
+
+model.compile(optimizer="adam",
+  loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+  metrics=["accuracy"]
+)
+
+num_epochs = 50
+
+# Save checkpoints after every 5 epochs
+cp_callback = tf.keras.callbacks.ModelCheckpoint(
+filepath="Checkpoint/cp.ckpt", 
+save_weights_only=True, 
+verbose=1,
+save_freq="epoch",
+period=5)
+
+# Train model
+model.fit(
+  train_set,
+  validation_data=test_set,
+  epochs=num_epochs,
+  callbacks=[cp_callback]
+)
+
+# Save model to disk
+model.save("CatClassifier.keras")
+model.save_weights('CatClassifierWeights.h5')
+print("Model saved.")
+
+# Load full model: model = tf.keras.models.load_model("CatClassifier.keras")
+# Load model weights: Create model architecture as above then call model.load_weights("CatClassifierWeights.h5")

CatClassifier.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed219bc27a1d57ddd90410e11587b204f9ec859b600092f86148b5a5590f1030
+size 120515920

GradioApp.py

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+import tensorflow as tf
+import gradio as gr
+import numpy as np
+import cv2
+import os
+
+classes = ["Abyssinian", "Bengal", "Birman", "Bombay", "British Shorthair", "Egyptian Mau", "Maine Coon", "Persian", "Ragdoll", "Russian Blue", "Siamese", "Sphynx"]
+example_images = ["examples/" + f for f in os.listdir("examples")]
+
+img_size = 400
+model = tf.keras.models.load_model("CatClassifier.keras")
+
+def model_predict(image):
+    image = cv2.resize(image, (img_size, img_size))
+    image = np.expand_dims(image, axis=0)
+
+    predictions = model.predict(image)
+    predictions = predictions[0]
+
+    predicted_class_index = np.argmax(predictions)
+    predicted_class = classes[predicted_class_index]
+    pred_dict = {}
+
+    for i in range(len(classes)):
+        pred_dict[classes[i]] = predictions[i]
+
+    return predicted_class, pred_dict
+
+
+def predict_breed(image):
+    if image is None:
+        return "Please attach an image first!", None
+    
+    return model_predict(image)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+          image_input = gr.Image(label="Cat Image")
+          run_button = gr.Button(variant="primary")
+          examples = gr.Examples(example_images,inputs=image_input)
+        with gr.Column():
+          breed_output = gr.Text(label="Predicted Breed", interactive=False)
+          predict_labels = gr.Label(label="Class Probabilties")  
+          
+    run_button.click(fn=predict_breed, inputs=image_input, outputs=[breed_output, predict_labels])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+tensorflow
+gradio
+numpy
+cv2