How to Create a Custom SSD Object Detection Model using Pre-Trained Image Classification Models from Tensorflow?

Are you tired of using pre-trained object detection models that don’t quite fit your specific needs? Do you want to create a custom SSD (Single Shot Detector) object detection model that can detect objects with precision and accuracy? Look no further! In this article, we’ll guide you through the process of creating a custom SSD object detection model using pre-trained image classification models from TensorFlow.

What is SSD Object Detection?

SSD (Single Shot Detector) is a popular object detection algorithm that detects objects in one single pass, unlike other algorithms like YOLO (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks) that require multiple passes. SSD is known for its speed, accuracy, and simplicity, making it a preferred choice for object detection tasks.

Why Use Pre-Trained Image Classification Models?

Pre-trained image classification models, such as MobileNet and ResNet, have been trained on large datasets and have learned to recognize features and patterns in images. By leveraging these pre-trained models, we can fine-tune them for object detection tasks, reducing the time and computational resources required to train a model from scratch.

Step 1: Install Required Libraries and Dependencies

Before we dive into the process, make sure you have the following libraries and dependencies installed:

• TensorFlow (TF) >= 2.3.0
• TensorFlow Object Detection (TF-OD) API
• OpenCV (optional but recommended for image processing)
• Python >= 3.6

You can install these libraries using pip:

pip install tensorflow tensorflow-object-detection-api opencv-python

Step 2: Prepare Your Dataset

Your dataset should consist of images with objects of interest annotated with bounding boxes. You can use tools like LabelImg or CVAT to annotate your images.

Create a folder structure for your dataset:

dataset/
  images/
    image1.jpg
    image2.jpg
    ...
  annotations/
    annotation1.xml
    annotation2.xml
    ...

Step 3: Convert Annotations to TFRecord Format

TFRecord is a binary format used by TensorFlow for data storage and retrieval. You can use the TF-OD API to convert your annotations to TFRecord format.

Create a Python script to convert your annotations:

import tensorflow as tf
from object_detection.utils import dataset_util

def create_tf_record(annotation_dir, image_dir, output_path):
    writer = tf.io.TFRecordWriter(output_path)
    
    for file in os.listdir(annotation_dir):
        annotation_file = os.path.join(annotation_dir, file)
        image_file = os.path.join(image_dir, file.replace('.xml', '.jpg'))
        
        with tf.io.gfile.GFile(image_file, 'rb') as fid:
            encoded_jpg = fid.read()
        
        encoded_jpg_io = io.BytesIO(encoded_jpg)
        image = PIL.Image.open(encoded_jpg_io)
        width, height = image.size
        
        annotation = ET.parse(annotation_file)
        root = annotation.getroot()
        
        for obj in root.findall('object'):
            obj_name = obj.find('name').text
            bndbox = obj.find('bndbox')
            xmin = float(bndbox.find('xmin').text)
            ymin = float(bndbox.find('ymin').text)
            xmax = float(bndbox.find('xmax').text)
            ymax = float(bndbox.find('ymax').text)
            
            tf_example = tf.train.Example(features=tf.train.Features(feature={
                'image/height': dataset_util.int64_feature(height),
                'image/width': dataset_util.int64_feature(width),
                'image/filename': dataset_util.bytes_feature(image_file.encode('utf8')),
                'image/source_id': dataset_util.bytes_feature(image_file.encode('utf8')),
                'image/encoded': dataset_util.bytes_feature(encoded_jpg),
                'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
                'image/object/bbox/xmin': dataset_util.float_list_feature([xmin / width]),
                'image/object/bbox/xmax': dataset_util.float_list_feature([xmax / width]),
                'image/object/bbox/ymin': dataset_util.float_list_feature([ymin / height]),
                'image/object/bbox/ymax': dataset_util.float_list_feature([ymax / height]),
                'image/object/class/label': dataset_util.int64_list_feature([1]),  # assuming class label is 1
                'image/object/class/text': dataset_util.bytes_list_feature([obj_name.encode('utf8')]),
            }))
            
            writer.write(tf_example.SerializeToString())
    
    writer.close()

annotation_dir = 'path/to/annotations'
image_dir = 'path/to/images'
output_path = 'path/to/output.tfrecord'

create_tf_record(annotation_dir, image_dir, output_path)

Step 4: Create a Custom SSD Model using Pre-Trained Image Classification Models

Now we’ll create a custom SSD model using a pre-trained image classification model as the base model.

Create a Python script to create the custom SSD model:

import tensorflow as tf
from object_detection.builders import model_builder
from object_detection.models import ssd_mobilenet_v2_coco

def create_custom_ssd_model(num_classes, image_size):
    base_model = ssd_mobilenet_v2_coco.SSDMobileNetV2COCO()
    base_model.provide_groundtruth = None
    
    img_input = tf.keras.layers.Input(shape=(image_size, image_size, 3), name='image')
    feature_extractor = base_model.get_image_feature_extractor()
    img_data = feature_extractor(img_input)
    
    anchor_generator = base_model.anchor_generator
    anchors = anchor_generator(img_data, img_input)
    
    box_predictor = tf.keras.layers.Conv2D(num_classes * 4, (3, 3), padding='same')(img_data)
    class_predictor = tf.keras.layers.Conv2D(num_classes, (3, 3), padding='same')(img_data)
    
    mbox_loc_output = tf.keras.layers.Reshape((-1, 4))(box_predictor)
    mbox_conf_output = tf.keras.layers.Reshape((-1, num_classes))(class_predictor)
    
    model = tf.keras.Model(inputs=img_input, outputs=[mbox_loc_output, mbox_conf_output])
    
    return model

num_classes = 1  # assuming we're detecting only one class
image_size = 300
custom_ssd_model = create_custom_ssd_model(num_classes, image_size)

Step 5: Train the Custom SSD Model

Now we’ll train the custom SSD model using our dataset.

Create a Python script to train the model:

import tensorflow as tf

def train_model(model, dataset, num_steps, batch_size):
    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    
    for step in range(num_steps):
        images, labels = dataset.batch(batch_size)
        with tf.GradientTape() as tape:
            predictions = model(images, training=True)
            loss = loss_fn(labels, predictions)
        
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    model.save('path/to/custom_ssd_model.h5')

dataset = tf.data.TFRecordDataset('path/to/output.tfrecord')
dataset = dataset.map(lambda x: tf.io.parse_single_example(x, features={
    'image/encoded': tf.io.FixedLenFeature((), tf.string),
    'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
    'image/object/class/label': tf.io.VarLenFeature(tf.int64),
}))

dataset = dataset.batch(32)
num_steps = 10000
train_model(custom_ssd_model, dataset, num_steps, 32)

Step 6: Evaluate and Test the Custom SSD Model

After training the model, we need to evaluate its performance using metrics like mAP (mean Average Precision) and AP (Average Precision).

Create a Python script to evaluate the model:

import tensorflow as tf
from object_detection.metrics import coco_evaluation

def evaluate_model(model, dataset):
    evaluator = coco_evaluation.CocoEvaluator(dataset)
    evaluator.add_metric(tf.keras.metrics.Accuracy(name='accuracy'))
    
    for images, labels in dataset.batch(32):
        predictions = model(images, training=False)
        evaluator.add_predictions(predictions)
    
    metrics = evaluator.evaluate()
    print(metrics)

evaluate_model(custom_ssd_model, dataset)

Conclusion

And that’s it! We’ve successfully created a custom SSD object detection model using a pre-trained image classification model from TensorFlow. By following these steps, you can create your own custom object

Frequently Asked Question

Get ready to unleash the power of custom SSD object detection models using pre-trained image classification models from TensorFlow!