Bird Species ML Classifier

Deep Learning Computer Vision TensorFlow Transfer Learning Custom Attention Model Optimization

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Case Study: Deep Learning Classification

Model Performance

91.23% Accuracy
117 Species
292,500 Images

Architecture

EfficientNetB3
Attention Layer
Transfer Learning

Training

48 Epochs
Mixed Precision
Gradient Clipping

This case study explores the development of a high-accuracy bird species classification model using deep learning techniques. The model achieves 91.23% accuracy across 117 species through careful architecture design, extensive data augmentation, and optimization strategies.

Development Objectives

Model Requirements

High accuracy fine-grained classification
Efficient inference for mobile deployment
Robust to varying image conditions

Technical Goals

Custom attention mechanism for detail focus
Mixed precision training optimization
Model size reduction for deployment

Data Processing Pipeline

Data Augmentation

Random rotation (±30°)
Random zoom (0.8-1.2x)
Horizontal flips
Color jittering

Image Preprocessing

Resolution standardization (300x300)
Normalization (-1 to 1 range)
Background noise reduction
Auto-contrast enhancement

Data Pipeline Implementation


class BirdDataPipeline:
    def __init__(self, image_size=300, batch_size=32):
        self.image_size = image_size
        self.batch_size = batch_size
        
        self.augmentation = tf.keras.Sequential([
            tf.keras.layers.RandomRotation(0.2),
            tf.keras.layers.RandomZoom(0.2),
            tf.keras.layers.RandomFlip("horizontal"),
            tf.keras.layers.RandomContrast(0.2)
        ])
        
    def preprocess_image(self, image):
        image = tf.image.resize(image, [self.image_size, self.image_size])
        image = tf.cast(image, tf.float32) / 127.5 - 1
        return image
        
    def build_dataset(self, image_paths, labels):
        dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels))
        return dataset.map(self.load_and_preprocess, num_parallel_calls=tf.data.AUTOTUNE) \
                     .batch(self.batch_size) \
                     .prefetch(tf.data.AUTOTUNE)
    
    def load_and_preprocess(self, path, label):
        image = tf.io.read_file(path)
        image = tf.image.decode_jpeg(image, channels=3)
        image = self.preprocess_image(image)
        if self.training:
            image = self.augmentation(image)
        return image, label

Model Architecture

Model Architecture Implementation


class AvianVisionModel(tf.keras.Model):
    def __init__(self, num_classes=117):
        super(AvianVisionModel, self).__init__()
        
        self.base_model = tf.keras.applications.EfficientNetB3(
            include_top=False,
            weights='imagenet',
            input_shape=(300, 300, 3)
        )
        
        # Fine-tuning settings
        self.base_model.trainable = True
        for layer in self.base_model.layers[:-30]:
            layer.trainable = False
            
        # Custom attention mechanism
        self.attention = self.build_attention_layer()
        
        # Classification head
        self.global_pool = tf.keras.layers.GlobalAveragePooling2D()
        self.dropout_1 = tf.keras.layers.Dropout(0.3)
        self.dense_1 = tf.keras.layers.Dense(512, activation='relu')
        self.batch_norm = tf.keras.layers.BatchNormalization()
        self.dropout_2 = tf.keras.layers.Dropout(0.4)
        self.output_layer = tf.keras.layers.Dense(num_classes, activation='softmax')

    def build_attention_layer(self):
        return tf.keras.Sequential([
            tf.keras.layers.Conv2D(512, 1, padding='same'),
            tf.keras.layers.BatchNormalization(),
            tf.keras.layers.Activation('relu'),
            tf.keras.layers.Conv2D(1, 1, padding='same'),
            tf.keras.layers.Activation('sigmoid')
        ])
        
    def call(self, inputs, training=False):
        x = self.base_model(inputs)
        
        # Apply attention mechanism
        attention_weights = self.attention(x)
        x = x * attention_weights
        
        # Classification pipeline
        x = self.global_pool(x)
        x = self.dropout_1(x, training=training)
        x = self.dense_1(x)
        x = self.batch_norm(x, training=training)
        x = self.dropout_2(x, training=training)
        
        return self.output_layer(x)

    def get_config(self):
        return {"num_classes": self.output_layer.units}

Model Training Process

Training Strategy

Progressive learning rate reduction
Mixed precision training
Gradient accumulation
Early stopping monitoring

Loss Functions

Categorical crossentropy
Label smoothing (0.1)
Focal loss for class imbalance
L2 regularization

Training Configuration


class TrainingConfig:
    def __init__(self):
        self.initial_lr = 1e-4
        self.min_lr = 1e-7
        self.warmup_epochs = 3
        self.weight_decay = 0.01
        self.label_smoothing = 0.1
        self.mixed_precision = True
        
    def get_callbacks(self):
        return [
            tf.keras.callbacks.EarlyStopping(
                monitor='val_accuracy',
                patience=5,
                restore_best_weights=True
            ),
            tf.keras.callbacks.ReduceLROnPlateau(
                monitor='val_loss',
                factor=0.5,
                patience=3,
                min_lr=self.min_lr
            ),
            tf.keras.callbacks.ModelCheckpoint(
                'best_model.h5',
                monitor='val_accuracy',
                save_best_only=True
            )
        ]
        
    def get_optimizer(self):
        return tf.keras.optimizers.Adam(
            learning_rate=self.initial_lr,
            weight_decay=self.weight_decay
        )

Performance Optimization

Model Optimization

Weight pruning (20% reduction)
Quantization-aware training
Layer fusion optimization
Memory footprint reduction

Inference Optimization

TFLite conversion
Int8 quantization
Operator optimization
Graph optimization

Model Optimization Implementation


class ModelOptimizer:
    def __init__(self, model):
        self.model = model
        self.input_shape = (300, 300, 3)
        
    def optimize_for_mobile(self):
        converter = tf.lite.TFLiteConverter.from_keras_model(self.model)
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.target_spec.supported_types = [tf.float16]
        converter.target_spec.supported_ops = [
            tf.lite.OpsSet.TFLITE_BUILTINS,
            tf.lite.OpsSet.SELECT_TF_OPS
        ]
        
        tflite_model = converter.convert()
        return tflite_model
        
    def quantize_model(self):
        converter = tf.lite.TFLiteConverter.from_keras_model(self.model)
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
        converter.inference_input_type = tf.uint8
        converter.inference_output_type = tf.uint8
        
        def representative_dataset():
            for _ in range(100):
                data = np.random.rand(1, *self.input_shape)
                yield [data.astype(np.float32)]
                
        converter.representative_dataset = representative_dataset
        quantized_model = converter.convert()
        return quantized_model