diff --git a/.gitignore b/.gitignore index a9f84f8..aa41af3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,3 @@ -/venv -/dataset -*.h5 - # Dependencies node_modules/ .pnp/ @@ -83,4 +79,4 @@ yarn-cache/ *.sublime-* .sass-cache parcel-cache.json -.pnp.* +.pnp.* \ No newline at end of file diff --git a/ML_SETUP_GUIDE.md b/ML_SETUP_GUIDE.md index 235bb0b..436bb61 100644 --- a/ML_SETUP_GUIDE.md +++ b/ML_SETUP_GUIDE.md @@ -12,7 +12,7 @@ The system uses a **Hybrid Pipeline** to balance performance on mobile devices w 2. **Classification**: Crops each detected card and passes it through two specialized TensorFlow.js models to determine the **Suit** and the **Value**. ## 2. Data Collection & Labeling -Because Jass cards have unique iconography, we use a custom dataset. +Because Jass cards have unique iconography, we use a custom dataset created from internet samples. ### Labeling Strategy: Folder-Based Annotation Instead of using bounding-box tools, we use the directory structure as labels. @@ -29,7 +29,7 @@ dataset/ ├── 6/ ├── 7/ ... - └── 14/ + └── 13/ ``` ### Process: diff --git a/convert_model.sh b/convert_model.sh deleted file mode 100755 index 28c4a34..0000000 --- a/convert_model.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash - -# Check if tensorflowjs is installed -if ! command -v tensorflowjs_converter &> /dev/null -then - echo "tensorflowjs_converter could not be found. Please install it using: pip install tensorflowjs" - exit 1 -fi - -MODEL_TYPE=$1 -INPUT_MODEL=$2 -OUTPUT_DIR=$3 - -if [ -z "$MODEL_TYPE" ] || [ -z "$INPUT_MODEL" ] || [ -z "$OUTPUT_DIR" ]; then - echo "Usage: ./convert_model.sh [suit|value] [path_to_h5_model] [output_directory]" - exit 1 -fi - -echo "Converting $MODEL_TYPE model from $INPUT_MODEL to $OUTPUT_DIR..." - -# Create output directory if it doesn't exist -mkdir -p "$OUTPUT_DIR" - -# Perform conversion -tensorflowjs_converter --input_format=keras "$INPUT_MODEL" "$OUTPUT_DIR" - -if [ $? -eq 0 ]; then - echo "Conversion successful. Model saved to $OUTPUT_DIR" -else - echo "Conversion failed." - exit 1 -fi diff --git a/public/models/suit_model/group1-shard1of3.bin b/public/models/suit_model/group1-shard1of3.bin deleted file mode 100644 index 709c270..0000000 Binary files a/public/models/suit_model/group1-shard1of3.bin and /dev/null differ diff --git a/public/models/suit_model/group1-shard2of3.bin b/public/models/suit_model/group1-shard2of3.bin deleted file mode 100644 index 7de3dce..0000000 Binary files a/public/models/suit_model/group1-shard2of3.bin and /dev/null differ diff --git a/public/models/suit_model/group1-shard3of3.bin b/public/models/suit_model/group1-shard3of3.bin deleted file mode 100644 index 4c581f8..0000000 Binary files a/public/models/suit_model/group1-shard3of3.bin and /dev/null differ diff --git a/public/models/suit_model/model.json b/public/models/suit_model/model.json deleted file mode 100644 index f3a2b55..0000000 --- a/public/models/suit_model/model.json +++ /dev/null @@ -1 +0,0 @@ -{"format": "layers-model", "generatedBy": "keras v3.14.1", "convertedBy": "TensorFlow.js Converter v4.22.0", "modelTopology": {"keras_version": "3.14.1", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "layers": [{"class_name": "InputLayer", "config": {"batch_shape": [null, 64, 64, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_layer_1", "optional": false}}, {"class_name": "Functional", "config": {"name": "mobilenetv2_1.00_224", "trainable": false, "layers": [{"class_name": "InputLayer", "config": {"batch_shape": [null, 64, 64, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_layer", "optional": false}, "name": "input_layer", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "Conv1", "trainable": false, "dtype": {"module": "keras", 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"shape": [192], "dtype": "float32"}, {"name": "block_5_depthwise_BN/beta", "shape": [192], "dtype": "float32"}, {"name": "block_5_depthwise_BN/moving_mean", "shape": [192], "dtype": "float32"}, {"name": "block_5_depthwise_BN/moving_variance", "shape": [192], "dtype": "float32"}, {"name": "block_5_project/kernel", "shape": [1, 1, 192, 32], "dtype": "float32"}, {"name": "block_5_project_BN/gamma", "shape": [32], "dtype": "float32"}, {"name": "block_5_project_BN/beta", "shape": [32], "dtype": "float32"}, {"name": "block_5_project_BN/moving_mean", "shape": [32], "dtype": "float32"}, {"name": "block_5_project_BN/moving_variance", "shape": [32], "dtype": "float32"}, {"name": "block_6_expand/kernel", "shape": [1, 1, 32, 192], "dtype": "float32"}, {"name": "block_6_expand_BN/gamma", "shape": [192], "dtype": "float32"}, {"name": "block_6_expand_BN/beta", "shape": [192], "dtype": "float32"}, {"name": "block_6_expand_BN/moving_mean", "shape": [192], "dtype": "float32"}, {"name": 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"dtype": "float32"}, {"name": "block_7_expand_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_7_expand_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_7_expand_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_7_depthwise/kernel", "shape": [3, 3, 384, 1], "dtype": "float32"}, {"name": "block_7_depthwise_BN/gamma", "shape": [384], "dtype": "float32"}, {"name": "block_7_depthwise_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_7_depthwise_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_7_depthwise_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_7_project/kernel", "shape": [1, 1, 384, 64], "dtype": "float32"}, {"name": "block_7_project_BN/gamma", "shape": [64], "dtype": "float32"}, {"name": "block_7_project_BN/beta", "shape": [64], "dtype": "float32"}, {"name": "block_7_project_BN/moving_mean", "shape": [64], "dtype": "float32"}, {"name": 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"dtype": "float32"}, {"name": "block_8_project_BN/beta", "shape": [64], "dtype": "float32"}, {"name": "block_8_project_BN/moving_mean", "shape": [64], "dtype": "float32"}, {"name": "block_8_project_BN/moving_variance", "shape": [64], "dtype": "float32"}, {"name": "block_9_expand/kernel", "shape": [1, 1, 64, 384], "dtype": "float32"}, {"name": "block_9_expand_BN/gamma", "shape": [384], "dtype": "float32"}, {"name": "block_9_expand_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_9_expand_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_9_expand_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_9_depthwise/kernel", "shape": [3, 3, 384, 1], "dtype": "float32"}, {"name": "block_9_depthwise_BN/gamma", "shape": [384], "dtype": "float32"}, {"name": "block_9_depthwise_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_9_depthwise_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_9_depthwise_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_9_project/kernel", "shape": [1, 1, 384, 64], "dtype": "float32"}, {"name": "block_9_project_BN/gamma", "shape": [64], "dtype": "float32"}, {"name": "block_9_project_BN/beta", "shape": [64], "dtype": "float32"}, {"name": "block_9_project_BN/moving_mean", "shape": [64], "dtype": "float32"}, {"name": "block_9_project_BN/moving_variance", "shape": [64], "dtype": "float32"}, {"name": "block_10_expand/kernel", "shape": [1, 1, 64, 384], "dtype": "float32"}, {"name": "block_10_expand_BN/gamma", "shape": [384], "dtype": "float32"}, {"name": "block_10_expand_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_10_expand_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_10_expand_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_10_depthwise/kernel", "shape": [3, 3, 384, 1], "dtype": "float32"}, {"name": "block_10_depthwise_BN/gamma", "shape": [384], "dtype": "float32"}, {"name": "block_10_depthwise_BN/beta", "shape": [384], "dtype": "float32"}, {"name": "block_10_depthwise_BN/moving_mean", "shape": [384], "dtype": "float32"}, {"name": "block_10_depthwise_BN/moving_variance", "shape": [384], "dtype": "float32"}, {"name": "block_10_project/kernel", "shape": [1, 1, 384, 96], "dtype": "float32"}, {"name": "block_10_project_BN/gamma", "shape": [96], "dtype": "float32"}, {"name": "block_10_project_BN/beta", "shape": [96], "dtype": "float32"}, {"name": "block_10_project_BN/moving_mean", "shape": [96], "dtype": "float32"}, {"name": "block_10_project_BN/moving_variance", "shape": [96], "dtype": "float32"}, {"name": "block_11_expand/kernel", "shape": [1, 1, 96, 576], "dtype": "float32"}, {"name": "block_11_expand_BN/gamma", "shape": [576], "dtype": "float32"}, {"name": "block_11_expand_BN/beta", "shape": [576], "dtype": "float32"}, {"name": "block_11_expand_BN/moving_mean", "shape": [576], "dtype": "float32"}, {"name": "block_11_expand_BN/moving_variance", "shape": [576], "dtype": "float32"}, {"name": "block_11_depthwise/kernel", "shape": [3, 3, 576, 1], "dtype": "float32"}, {"name": "block_11_depthwise_BN/gamma", "shape": [576], "dtype": "float32"}, {"name": "block_11_depthwise_BN/beta", "shape": [576], "dtype": "float32"}, {"name": "block_11_depthwise_BN/moving_mean", "shape": [576], "dtype": "float32"}, {"name": "block_11_depthwise_BN/moving_variance", "shape": [576], "dtype": "float32"}, {"name": "block_11_project/kernel", "shape": [1, 1, 576, 96], "dtype": "float32"}, {"name": "block_11_project_BN/gamma", "shape": [96], "dtype": "float32"}, {"name": "block_11_project_BN/beta", "shape": [96], "dtype": "float32"}, {"name": "block_11_project_BN/moving_mean", "shape": [96], "dtype": "float32"}, {"name": "block_11_project_BN/moving_variance", "shape": [96], "dtype": "float32"}, {"name": "block_12_expand/kernel", "shape": [1, 1, 96, 576], "dtype": "float32"}, {"name": "block_12_expand_BN/gamma", "shape": [576], "dtype": "float32"}, {"name": "block_12_expand_BN/beta", "shape": [576], "dtype": "float32"}, {"name": "block_12_expand_BN/moving_mean", "shape": [576], "dtype": "float32"}, {"name": "block_12_expand_BN/moving_variance", "shape": [576], "dtype": "float32"}, {"name": "block_12_depthwise/kernel", "shape": [3, 3, 576, 1], "dtype": "float32"}, {"name": "block_12_depthwise_BN/gamma", "shape": [576], "dtype": "float32"}, {"name": "block_12_depthwise_BN/beta", "shape": [576], "dtype": "float32"}, {"name": "block_12_depthwise_BN/moving_mean", "shape": [576], "dtype": "float32"}, {"name": "block_12_depthwise_BN/moving_variance", "shape": [576], "dtype": "float32"}, {"name": "block_12_project/kernel", "shape": [1, 1, 576, 96], "dtype": "float32"}, {"name": "block_12_project_BN/gamma", "shape": [96], "dtype": "float32"}, {"name": "block_12_project_BN/beta", "shape": [96], "dtype": "float32"}, {"name": "block_12_project_BN/moving_mean", "shape": [96], "dtype": "float32"}, {"name": 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"shape": [160], "dtype": "float32"}, {"name": "block_13_project_BN/beta", "shape": [160], "dtype": "float32"}, {"name": "block_13_project_BN/moving_mean", "shape": [160], "dtype": "float32"}, {"name": "block_13_project_BN/moving_variance", "shape": [160], "dtype": "float32"}, {"name": "block_14_expand/kernel", "shape": [1, 1, 160, 960], "dtype": "float32"}, {"name": "block_14_expand_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_14_expand_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_14_expand_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_14_expand_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_14_depthwise/kernel", "shape": [3, 3, 960, 1], "dtype": "float32"}, {"name": "block_14_depthwise_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_14_depthwise_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_14_depthwise_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_14_depthwise_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_14_project/kernel", "shape": [1, 1, 960, 160], "dtype": "float32"}, {"name": "block_14_project_BN/gamma", "shape": [160], "dtype": "float32"}, {"name": "block_14_project_BN/beta", "shape": [160], "dtype": "float32"}, {"name": "block_14_project_BN/moving_mean", "shape": [160], "dtype": "float32"}, {"name": "block_14_project_BN/moving_variance", "shape": [160], "dtype": "float32"}, {"name": "block_15_expand/kernel", "shape": [1, 1, 160, 960], "dtype": "float32"}, {"name": "block_15_expand_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_15_expand_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_15_expand_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_15_expand_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_15_depthwise/kernel", "shape": [3, 3, 960, 1], "dtype": "float32"}, {"name": "block_15_depthwise_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_15_depthwise_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_15_depthwise_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_15_depthwise_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_15_project/kernel", "shape": [1, 1, 960, 160], "dtype": "float32"}, {"name": "block_15_project_BN/gamma", "shape": [160], "dtype": "float32"}, {"name": "block_15_project_BN/beta", "shape": [160], "dtype": "float32"}, {"name": "block_15_project_BN/moving_mean", "shape": [160], "dtype": "float32"}, {"name": "block_15_project_BN/moving_variance", "shape": [160], "dtype": "float32"}, {"name": "block_16_expand/kernel", "shape": [1, 1, 160, 960], "dtype": "float32"}, {"name": "block_16_expand_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_16_expand_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_16_expand_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_16_expand_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_16_depthwise/kernel", "shape": [3, 3, 960, 1], "dtype": "float32"}, {"name": "block_16_depthwise_BN/gamma", "shape": [960], "dtype": "float32"}, {"name": "block_16_depthwise_BN/beta", "shape": [960], "dtype": "float32"}, {"name": "block_16_depthwise_BN/moving_mean", "shape": [960], "dtype": "float32"}, {"name": "block_16_depthwise_BN/moving_variance", "shape": [960], "dtype": "float32"}, {"name": "block_16_project/kernel", "shape": [1, 1, 960, 320], "dtype": "float32"}, {"name": "block_16_project_BN/gamma", "shape": [320], "dtype": "float32"}, {"name": "block_16_project_BN/beta", "shape": [320], "dtype": "float32"}, {"name": "block_16_project_BN/moving_mean", "shape": [320], "dtype": "float32"}, {"name": "block_16_project_BN/moving_variance", "shape": [320], "dtype": "float32"}, {"name": "Conv_1/kernel", "shape": [1, 1, 320, 1280], "dtype": "float32"}, {"name": "Conv_1_bn/gamma", "shape": [1280], "dtype": "float32"}, {"name": "Conv_1_bn/beta", "shape": [1280], "dtype": "float32"}, {"name": "Conv_1_bn/moving_mean", "shape": [1280], "dtype": "float32"}, {"name": "Conv_1_bn/moving_variance", "shape": [1280], "dtype": "float32"}]}]} \ No newline at end of file diff --git a/src/App.jsx b/src/App.jsx index 2d48585..2b430a2 100644 --- a/src/App.jsx +++ b/src/App.jsx @@ -18,6 +18,15 @@ const App = () => { ); }; + +const App = () => { + return ( + + + + ); +}; + const AppContent = () => { const { gameState } = useGameStateContext(); diff --git a/src/components/Detection/Detection.tsx b/src/components/Detection/Detection.tsx index 8cdcf98..0df89c2 100644 --- a/src/components/Detection/Detection.tsx +++ b/src/components/Detection/Detection.tsx @@ -96,45 +96,39 @@ const Detection: React.FC = ({ videoRef, canvasRef, onCardsDetec // Enhanced card region detection specialized for Jass cards - const findCardRegions = (imageData: ImageData, width: number, height: number): {x: number, y: number, width: number, height: number}[] => { - const regions = []; - const step = 24; // Increased step to reduce noise and overlapping detections - - for (let y = 0; y < height; y += step) { - for (let x = 0; x < width; x += step) { - const i = (y * width + x) * 4; - const r = imageData.data[i]; - const g = imageData.data[i + 1]; - const b = imageData.data[i + 2]; - const brightness = (r + g + b) / 3; - - if (brightness > 200 && brightness < 255) { - const region = getCardRegionWithShapeAnalysis(imageData, width, height, x, y, step); - if (region && region.width > 60 && region.height > 80) { - const aspectRatio = region.width / region.height; - if (aspectRatio > 0.6 && aspectRatio < 1.4) { - regions.push(region); - } + const findCardRegions = (imageData: ImageData, width: number, height: number): {x: number, y: number, width: number, height: number}[] => { + const regions = []; + const step = 12; // Adjusted step for better detection + + // Look for card-colored regions: typically white/gray cards with identifiable suit symbols + for (let y = 0; y < height; y += step) { + for (let x = 0; x < width; x += step) { + const i = (y * width + x) * 4; + const r = imageData.data[i]; + const g = imageData.data[i + 1]; + const b = imageData.data[i + 2]; + const brightness = (r + g + b) / 3; + + // Card background brightness range (light cards) + if (brightness > 180 && brightness < 250) { + const region = getCardRegionWithShapeAnalysis(imageData, width, height, x, y, step); + if (region && region.width > 30 && region.height > 40) { + // Check that this is a proper rectangular card area by testing aspect ratio + const widthDiff = region.width; + const heightDiff = region.height; + const aspectRatio = widthDiff / heightDiff; + + // Typical playing card aspect ratio (roughly 0.7 to 1.3) + if (aspectRatio > 0.7 && aspectRatio < 1.3) { + regions.push(region); } } } } - - // Remove overlapping regions to avoid "dozens of nonsense cards" - const uniqueRegions = []; - regions.sort((a, b) => (a.width * a.height) - (b.width * b.height)); - - for (const region of regions) { - const isOverlapping = uniqueRegions.some(u => - Math.abs(u.x - region.x) < 30 && Math.abs(u.y - region.y) < 30 - ); - if (!isOverlapping) { - uniqueRegions.push(region); - } - } - - return uniqueRegions; - }; + } + + return regions; + }; // Improved card region extraction with shape analysis for more accurate detection const getCardRegionWithShapeAnalysis = (imageData: ImageData, width: number, height: number, x: number, y: number, step: number): {x: number, y: number, width: number, height: number} | null => { diff --git a/src/components/Results/ResultsScreen.tsx b/src/components/Results/ResultsScreen.tsx index c2f26ef..99b9d53 100644 --- a/src/components/Results/ResultsScreen.tsx +++ b/src/components/Results/ResultsScreen.tsx @@ -77,19 +77,14 @@ const ResultsScreen: React.FC = () => {

Detected Cards

-
- {gameState.detectedCards.map(card => ( -
-
- {card.suit === 'Schellen' && '🔔'} - {card.suit === 'Schilten' && '🛡️'} - {card.suit === 'Eicheln' && '🌰'} - {card.suit === 'Rosen' && '🌹'} -
-
{card.value || '?'}pts
-
- ))} -
+
+ {gameState.detectedCards.map(card => ( +
+
♠
+
{card.value || '?'}pts
+
+ ))} +
diff --git a/src/services/CardModelService.ts b/src/services/CardModelService.ts index f6261f6..860070b 100644 --- a/src/services/CardModelService.ts +++ b/src/services/CardModelService.ts @@ -13,7 +13,7 @@ class CardModelService { private readonly VALUE_MODEL_PATH = '/models/value_model/model.json'; private readonly SUIT_LABELS = ['Schellen', 'Schilten', 'Eicheln', 'Rosen']; - private readonly VALUE_LABELS = ['6', '7', '8', '9', '10', '11', '12', '13', '14']; + private readonly VALUE_LABELS = ['6', '7', '8', '9', '10', '11', '12', '13']; async init(): Promise { try { diff --git a/train_model.py b/train_model.py deleted file mode 100644 index e6a3c16..0000000 --- a/train_model.py +++ /dev/null @@ -1,82 +0,0 @@ -import os -import tensorflow as tf -from tensorflow.keras import layers, models, optimizers -from tensorflow.keras.preprocessing.image import ImageDataGenerator - -def create_model(num_classes): - # Use MobileNetV2 as the base model - base_model = tf.keras.applications.MobileNetV2( - input_shape=(64, 64, 3), - include_top=False, - weights='imagenet' - ) - base_model.trainable = False # Freeze base model for transfer learning - - model = models.Sequential([ - base_model, - layers.GlobalAveragePooling2D(), - layers.Dropout(0.2), - layers.Dense(num_classes, activation='softmax') - ]) - - model.compile( - optimizer=optimizers.Adam(), - loss='categorical_crossentropy', - metrics=['accuracy'] - ) - return model - -def train(model_type, dataset_root, output_path): - # Set parameters based on model type - if model_type == 'suit': - num_classes = 4 - elif model_type == 'value': - num_classes = 9 - else: - raise ValueError("model_type must be 'suit' or 'value'") - - # Data augmentation as per ML_SETUP_GUIDE.md - datagen = ImageDataGenerator( - rescale=1./255, - rotation_range=15, - brightness_range=[0.8, 1.2], - validation_split=0.2 - ) - - train_generator = datagen.flow_from_directory( - os.path.join(dataset_root, f'{model_type}_model'), - target_size=(64, 64), - batch_size=32, - class_mode='categorical', - subset='training' - ) - - validation_generator = datagen.flow_from_directory( - os.path.join(dataset_root, f'{model_type}_model'), - target_size=(64, 64), - batch_size=32, - class_mode='categorical', - subset='validation' - ) - - model = create_model(num_classes) - - print(f"Training {model_type} model...") - model.fit( - train_generator, - epochs=20, - validation_data=validation_generator - ) - - model.save(output_path) - print(f"Model saved to {output_path}") - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--type', choices=['suit', 'value'], required=True) - parser.add_argument('--dataset', default='dataset') - parser.add_argument('--output', required=True) - args = parser.parse_args() - - train(args.type, args.dataset, args.output)