Add convnext mode and weights.

Models/verify_model_sk_convnext_v1.py

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+import os
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')  # Headless backend
+import matplotlib.pyplot as plt
+from PIL import Image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.convnext import preprocess_input
+from sklearn.metrics import confusion_matrix, accuracy_score, ConfusionMatrixDisplay
+
+# ------------------------------
+# This is the verification for convnext_20260228_90.h5.keras
+# We are currently at 99% accuracy in the predictions with the data
+# ------------------------------
+
+
+# ----------------------------
+# Paths
+# ----------------------------
+# MODEL_PATH = "/home/pi/CNN/Weights/convnext_20260228_90.h5.keras"
+# TEST_DIR = "/home/pi/DeepLearning/Data"  # test images structured by class
+# OUTPUT_DIR = "/home/pi/CNN/Models/evaluation_outputs"
+
+MODEL_PATH = "c:\boneyard\DeepLearning\CNN\Weights\convnext_20260228_90.h5.keras"
+TEST_DIR = "c:\boneyard\DeepLearning\CNN\Data"  # test images structured by class
+OUTPUT_DIR = "c:\boneyard\DeepLearning\CNN\Models\evaluation_outputs"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ----------------------------
+# Suppress TensorFlow warnings
+# ----------------------------
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # only errors
+
+# ----------------------------
+# Test Data Generator
+# ----------------------------
+test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
+
+test_generator = test_datagen.flow_from_directory(
+    TEST_DIR,
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='binary',
+    shuffle=False
+)
+
+# ----------------------------
+# Load Model
+# ----------------------------
+model = load_model(MODEL_PATH)
+print("Model loaded successfully.")
+
+# ----------------------------
+# Predictions
+# ----------------------------
+y_true = test_generator.classes
+y_pred_probs = model.predict(test_generator, verbose=1)
+y_pred = (y_pred_probs > 0.5).astype(int).reshape(-1)
+
+# ----------------------------
+# Accuracy
+# ----------------------------
+accuracy = accuracy_score(y_true, y_pred)
+print(f"Test Accuracy: {accuracy*100:.2f}%")
+
+# ----------------------------
+# Confusion Matrix
+# ----------------------------
+cm = confusion_matrix(y_true, y_pred)
+disp = ConfusionMatrixDisplay(
+    confusion_matrix=cm,
+    display_labels=list(test_generator.class_indices.keys())
+)
+disp.plot(cmap=plt.cm.Blues)
+plt.title("Confusion Matrix")
+conf_matrix_path = os.path.join(OUTPUT_DIR, "confusion_matrix.png")
+plt.savefig(conf_matrix_path, dpi=300)
+plt.close()
+print(f"Confusion matrix saved to: {conf_matrix_path}")
+
+# ----------------------------
+# Misclassified Images
+# ----------------------------
+misclassified_idx = np.where(y_true != y_pred)[0]
+print(f"Number of misclassified images: {len(misclassified_idx)}")
+
+MAX_IMAGES = 20  # Number of misclassified images to save
+for i, idx in enumerate(misclassified_idx[:MAX_IMAGES]):
+    img_path = test_generator.filepaths[idx]
+
+    # Open image with Pillow
+    img = Image.open(img_path).convert('RGB')
+
+    plt.imshow(np.array(img))
+    true_label = list(test_generator.class_indices.keys())[y_true[idx]]
+    pred_label = list(test_generator.class_indices.keys())[y_pred[idx]]
+    plt.title(f"True: {true_label}, Pred: {pred_label}")
+    plt.axis('off')
+
+    save_path = os.path.join(OUTPUT_DIR, f"misclassified_{i+1}.png")
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+    print(f"Saved misclassified image: {save_path}")