diff --git a/Python/new_pokedex_resnet50.py b/Python/new_pokedex_resnet50.py
deleted file mode 100644
index bd105bb1585a3a721bcfb1a677107bf61899ceb1..0000000000000000000000000000000000000000
--- a/Python/new_pokedex_resnet50.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras.applications import ResNet50
-from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
-from tensorflow.keras.models import Model
-from tensorflow.keras.preprocessing import image_dataset_from_directory
-import json
-import os
-
-# --- GPU Strategy ---
-strategy = tf.distribute.MirroredStrategy()
-print("Number of GPUs:", strategy.num_replicas_in_sync)
-
-# --- Paths ---
-dataset_path = "/home/users/d/divia/scratch/Combined_Dataset"
-model_output_path = "/home/users/d/divia/ResNet50"
-os.makedirs(model_output_path, exist_ok=True)
-
-# --- Image settings ---
-img_size = (224, 224)
-batch_size = 32
-
-# --- Load datasets ---
-raw_train_ds = image_dataset_from_directory(
- dataset_path,
- image_size=img_size,
- batch_size=batch_size,
- validation_split=0.2,
- subset="training",
- seed=123,
-)
-
-raw_val_ds = image_dataset_from_directory(
- dataset_path,
- image_size=img_size,
- batch_size=batch_size,
- validation_split=0.2,
- subset="validation",
- seed=123,
-)
-
-# Save class names
-class_names = raw_train_ds.class_names
-with open(os.path.join(model_output_path, "class_names.json"), "w") as f:
- json.dump(class_names, f)
-print(f"Detected {len(class_names)} Pokémon classes.")
-
-# --- Performance improvements ---
-AUTOTUNE = tf.data.AUTOTUNE
-train_ds = raw_train_ds.prefetch(buffer_size=AUTOTUNE)
-val_ds = raw_val_ds.prefetch(buffer_size=AUTOTUNE)
-
-# --- Build the model ---
-with strategy.scope():
- base_model = ResNet50(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
- x = base_model.output
- x = GlobalAveragePooling2D()(x)
- outputs = Dense(len(class_names), activation="softmax")(x)
-
- model = Model(inputs=base_model.input, outputs=outputs)
-
- # Freeze base model
- for layer in base_model.layers:
- layer.trainable = False
-
- model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-
-# --- Train the model ---
-model.fit(train_ds, validation_data=val_ds, epochs=10)
-
-# --- Optional Fine-tuning (Unfreeze top layers) ---
-# Uncomment to fine-tune top layers of ResNet50
-for layer in base_model.layers[-30:]:
- layer.trainable = True
-model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-model.fit(train_ds, validation_data=val_ds, epochs=5)
-
-# --- Save the model ---
-model_h5_path = os.path.join(model_output_path, "pokemon_resnet50.h5")
-model.save(model_h5_path)
-print(f"Model saved to {model_h5_path}")
-
-# --- Save as TensorFlow SavedModel (for ONNX export) ---
-saved_model_path = os.path.join(model_output_path, "saved_model")
-tf.saved_model.save(model, saved_model_path)
-print(f"SavedModel exported to {saved_model_path}")
diff --git a/Python/pokedex_ResNet50.py b/Python/pokedex_ResNet50.py
index 29b498d374c154b1cf7ef6d06f5a58fe1150378f..59886992752f6d21bb07db0e88a3b6bfcb647599 100644
--- a/Python/pokedex_ResNet50.py
+++ b/Python/pokedex_ResNet50.py
@@ -1,93 +1,116 @@
-import keras
import tensorflow as tf
-from keras import layers
-from tensorflow import data as tf_data
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
+from tensorflow.keras.models import Model
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+from tensorflow.keras import layers
+from tensorflow.keras.callbacks import EarlyStopping
+from sklearn.utils.class_weight import compute_class_weight
+import numpy as np
+import json
+import os
# --- GPU Strategy ---
strategy = tf.distribute.MirroredStrategy()
print("Number of GPUs:", strategy.num_replicas_in_sync)
-# --- Parameters ---
-data_dir = "/home/users/d/divia/scratch/Combined_Dataset"
-image_size = (224, 224)
-num_classes = 151
-base_batch_size = 32
-base_lr = 1e-3
-
-global_batch_size = 32
-scaled_lr = min(base_lr * (global_batch_size / base_batch_size), 1e-3)
-
-# --- Load Dataset ---
-full_ds = keras.utils.image_dataset_from_directory(
- data_dir,
- labels="inferred",
- label_mode="int",
- image_size=image_size,
- batch_size=global_batch_size,
- shuffle=True,
- seed=1234
-)
+# --- Paths ---
+dataset_path = "/home/padi/Git/pokedex/Combined_Dataset"
+model_output_path = "/home/padi/Git/pokedex/ResNet50"
+os.makedirs(model_output_path, exist_ok=True)
-# --- Train/Val Split ---
-total_batches = tf.data.experimental.cardinality(full_ds).numpy()
-train_size = int(0.8 * total_batches)
-train_ds = full_ds.take(train_size)
-val_ds = full_ds.skip(train_size)
+# --- Image settings ---
+img_size = (224, 224)
+batch_size = 32
# --- Data Augmentation ---
-data_augmentation_layers = keras.Sequential([
+data_augmentation = tf.keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
+ layers.RandomZoom(0.1),
+ layers.RandomContrast(0.1),
])
-def preprocess_train(img, label):
- img = data_augmentation_layers(img)
- label = tf.one_hot(label, num_classes)
- return img, label
+# --- Load datasets ---
+raw_train_ds = image_dataset_from_directory(
+ dataset_path,
+ image_size=img_size,
+ batch_size=batch_size,
+ validation_split=0.2,
+ subset="training",
+ seed=123,
+)
-def preprocess_val(img, label):
- label = tf.one_hot(label, num_classes)
- return img, label
+raw_val_ds = image_dataset_from_directory(
+ dataset_path,
+ image_size=img_size,
+ batch_size=batch_size,
+ validation_split=0.2,
+ subset="validation",
+ seed=123,
+)
-train_ds = train_ds.map(preprocess_train, num_parallel_calls=tf_data.AUTOTUNE)
-val_ds = val_ds.map(preprocess_val, num_parallel_calls=tf_data.AUTOTUNE)
+# Save class names
+class_names = raw_train_ds.class_names
+with open(os.path.join(model_output_path, "class_names.json"), "w") as f:
+ json.dump(class_names, f)
+print(f"Detected {len(class_names)} Pokémon classes.")
+
+# --- Compute class weights ---
+print("Computing class weights...")
+all_labels = []
+for _, labels in raw_train_ds.unbatch():
+ all_labels.append(labels.numpy())
+
+all_labels = np.array(all_labels)
+class_weights = compute_class_weight(
+ class_weight="balanced",
+ classes=np.unique(all_labels),
+ y=all_labels
+)
+class_weight_dict = dict(enumerate(class_weights))
+print("Class weights ready.")
-train_ds = train_ds.prefetch(buffer_size=tf_data.AUTOTUNE)
-val_ds = val_ds.prefetch(buffer_size=tf_data.AUTOTUNE)
+# --- Performance improvements ---
+AUTOTUNE = tf.data.AUTOTUNE
+train_ds = raw_train_ds.map(lambda x, y: (data_augmentation(x), y)).prefetch(AUTOTUNE)
+val_ds = raw_val_ds.prefetch(buffer_size=AUTOTUNE)
-# --- Build & Compile Model ---
+# --- Build the model ---
with strategy.scope():
- base_model = tf.keras.applications.ResNet50(
- include_top=False,
- weights='imagenet',
- input_shape=(224, 224, 3)
- )
-
- model = keras.Sequential([
- base_model,
- layers.GlobalAveragePooling2D(),
- layers.Dense(256, activation='relu'),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation='softmax')
- ])
-
- optimizer = tf.keras.optimizers.Adam(learning_rate=scaled_lr)
-
- model.compile(
- optimizer=optimizer,
- loss='categorical_crossentropy',
- metrics=['accuracy']
- )
-
-# --- Train ---
+ base_model = ResNet50(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
+ x = base_model.output
+ x = GlobalAveragePooling2D()(x)
+ outputs = Dense(len(class_names), activation="softmax")(x)
+
+ model = Model(inputs=base_model.input, outputs=outputs)
+
+ # Freeze some layers
+ for layer in base_model.layers[:100]:
+ layer.trainable = False
+
+ model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# --- Callbacks ---
callbacks = [
- keras.callbacks.ModelCheckpoint("/home/users/d/divia/ResNet50/save_at_{epoch}.h5"),
- keras.callbacks.EarlyStopping(monitor="val_loss", patience=2, restore_best_weights=True)
+ EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
]
+# --- Train the model with class weights ---
model.fit(
train_ds,
validation_data=val_ds,
- epochs=15,
- callbacks=callbacks
-)
\ No newline at end of file
+ epochs=10,
+ callbacks=callbacks,
+ class_weight=class_weight_dict
+)
+
+# --- Save the model ---
+model_h5_path = os.path.join(model_output_path, "pokemon_resnet50.h5")
+model.save(model_h5_path)
+print(f"Model saved to {model_h5_path}")
+
+# --- Save as TensorFlow SavedModel (for ONNX export) ---
+saved_model_path = os.path.join(model_output_path, "saved_model")
+tf.saved_model.save(model, saved_model_path)
+print(f"SavedModel exported to {saved_model_path}")
\ No newline at end of file
diff --git a/Python/test_ResNet50.py b/Python/test_ResNet50.py
new file mode 100644
index 0000000000000000000000000000000000000000..04dd17289f4678570ac94118685fd594f51ef6ab
--- /dev/null
+++ b/Python/test_ResNet50.py
@@ -0,0 +1,61 @@
+from tensorflow import keras
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import random
+import json
+
+# === Load class names from JSON ===
+with open("../models/ResNet50/class_names.json", "r") as f:
+ class_names = json.load(f)
+ class_names = [class_names[i] for i in range(len(class_names))] # convert to list
+
+# === Load trained model ===
+model = keras.models.load_model("../models/ResNet50/pokemon_resnet50.h5")
+
+# === Paths ===
+base_path = "../Combined_Dataset"
+
+# === Prepare 2x2 Plot ===
+plt.figure(figsize=(10, 10))
+
+for i in range(4):
+ # Pick random class and image
+ random_class = random.choice(class_names)
+ class_folder = os.path.join(base_path, random_class)
+ random_image = random.choice([
+ f for f in os.listdir(class_folder)
+ if f.lower().endswith(('.png', '.jpg', '.jpeg'))
+ ])
+ img_path = os.path.join(class_folder, random_image)
+
+ # === Load & Preprocess Image ===
+ img = keras.utils.load_img(img_path, target_size=(224, 224)) # resize to match model input
+ img_array = keras.utils.img_to_array(img)
+ img_array = img_array / 255.0 # normalize if your model expects it
+ img_array = tf.expand_dims(img_array, 0)
+
+ # === Predict ===
+ predictions = model.predict(img_array, verbose=0)
+ probabilities = tf.nn.softmax(predictions[0])
+ predicted_class_index = np.argmax(probabilities)
+ predicted_label = class_names[predicted_class_index]
+ confidence = 100 * probabilities[predicted_class_index]
+
+ # Compare with actual
+ is_correct = predicted_label == random_class
+
+ # === Plot ===
+ ax = plt.subplot(2, 2, i + 1)
+ plt.imshow(img)
+ plt.axis("off")
+ plt.title(
+ f"Pred: {predicted_label}\n"
+ f"True: {random_class}\n"
+ f"{'YES' if is_correct else 'NO'} | {confidence:.1f}%",
+ fontsize=10
+ )
+
+plt.tight_layout()
+plt.show()
diff --git a/ResNet50/class_names.json b/ResNet50/class_names.json
new file mode 100644
index 0000000000000000000000000000000000000000..57874547cdef98b4f08dd1eebb09a2dcedc3ed4e
--- /dev/null
+++ b/ResNet50/class_names.json
@@ -0,0 +1 @@
+["Abo", "Abra", "Akwakwak", "Alakazam", "Amonistar", "Amonita", "Aquali", "Arbok", "Arcanin", "Artikodin", "Aspicot", "A\u00e9romite", "Boustiflor", "Bulbizarre", "Caninos", "Carabaffe", "Carapuce", "Chenipan", "Chrysacier", "Ch\u00e9tiflor", "Coconfort", "Colossinge", "Crustabri", "Dardargnan", "Dodrio", "Doduo", "Dracaufeu", "Draco", "Dracolosse", "Ectoplasma", "Empiflor", "Excelangue", "Fantominus", "Farfetchd", "Feunard", "Flagadoss", "Florizarre", "F\u00e9rosinge", "Galopa", "Goupix", "Gravalanch", "Grodoudou", "Grolem", "Grotadmorv", "Herbizarre", "Hypnomade", "Hypoc\u00e9an", "Hypotrempe", "Ins\u00e9cateur", "Kabuto", "Kabutops", "Kadabra", "Kangourex", "Kicklee", "Kokiyas", "Krabboss", "Krabby", "Lamantine", "Leveinard", "Lippoutou", "Lokhlass", "L\u00e9viator", "M. Mime", "Machoc", "Machopeur", "Mackogneur", "Magicarpe", "Magmar", "Magn\u00e9ti", "Magn\u00e9ton", "Mew", "Mewtwo", "Miaouss", "Mimitoss", "Minidraco", "Mystherbe", "M\u00e9lodelfe", "M\u00e9lof\u00e9e", "M\u00e9tamorph", "Nidoking", "Nidoqueen", "Nidoran_femelle", "Nidoran_male", "Nidorina", "Nidorino", "Noadkoko", "Noeunoeuf", "Nosferalto", "Nosferapti", "Onix", "Ortide", "Ossatueur", "Osselait", "Otaria", "Papilusion", "Paras", "Parasect", "Persian", "Piafabec", "Pikachu", "Poissir\u00e8ne", "Poissoroy", "Ponyta", "Porygon", "Psykokwak", "Ptitard", "Pt\u00e9ra", "Pyroli", "Racaillou", "Rafflesia", "Raichu", "Ramoloss", "Rapasdepic", "Rattata", "Rattatac", "Reptincel", "Rhinocorne", "Rhinof\u00e9ros", "Rondoudou", "Ronflex", "Roucarnage", "Roucool", "Roucoups", "Sabelette", "Sablaireau", "Salam\u00e8che", "Saquedeneu", "Scarabrute", "Smogo", "Smogogo", "Soporifik", "Spectrum", "Stari", "Staross", "Sulfura", "Tadmorv", "Tartard", "Taupiqueur", "Tauros", "Tentacool", "Tentacruel", "Tortank", "Triopikeur", "Tygnon", "T\u00eatarte", "Voltali", "Voltorbe", "\u00c9lecthor", "\u00c9lectrode", "\u00c9lektek", "\u00c9voli"]
\ No newline at end of file