Transfer learning in computer vision with TensorFlow Hu

作者：禅与计算机程序设计艺术

1.简介

Transfer learning represents a machine learning approach that enables models to acquire new knowledge from previously trained models on related tasks. This technique is particularly valuable for applications like image classification, object detection, and speech recognition. However, transfer learning presents several challenges, including data availability issues, model complexity, and the substantial resource requirements involved in training. In this article, we will delve into utilizing TensorFlow Hub (TF-Hub) for implementing transfer learning in computer vision contexts. TF-Hub offers pre-trained models developed on extensive datasets; these models can be adapted to specific tasks through retraining with smaller datasets. This process simplifies leveraging pre-trained models for transfer learning while preserving their capacity to generalize across new domains or tasks. The article will demonstrate practical applications of transfer learning techniques using TF-Hub in three distinct scenarios:

1. Image Classification
2. Object Detection
3. Text Embeddings We hope that through our detailed explanations, examples, and case studies, readers gain a better understanding of how to effectively utilize transfer learning techniques in various computer vision applications and also contribute to further research on transfer learning methods.

2.基本概念术语说明

2.1 Transfer Learning

Transfer learning involves a machine learning technique where a model is initially trained on a source dataset, such as D1, before being adapted for use on another related but distinct dataset, D2. The objective is to facilitate faster and more accurate knowledge acquisition by leveraging insights gained from D1 rather than starting afresh with D2. A primary benefit of transfer learning over traditional deep neural network training lies in its ability to significantly reduce training time since most relevant information is already embedded within pre-trained layers. Transfer learning minimizes reliance on labeled data due to its low cost and high accuracy when executed appropriately. Another notable characteristic of transfer learning is its capacity to address intricate scenarios where source and target domains are not clearly delineated. Finally, transfer learning enhances scalability and portability across various platforms by enabling consistent application of identical model architectures across diverse tasks.

2.2 Pre-Trained Models

Pre-trained models are typically those that have been extensively trained on datasets containing tens of millions of images and other types of data. These models are specifically designed to excel in core functionalities such as image classification, object detection, and language modeling. They have acquired a wide range of insights into visual elements, which enables them to address complex challenges involving similar types of input data. Their application significantly reduces the computational resources required for developing tailored solutions, thereby cutting down both processing power and storage demands. Additionally, these models offer an excellent foundation for transfer learning since their weights effectively encapsulate key visual attributes that are fundamental to certain concepts in computer vision.

2.3 Fine-Tuning

Fine-tuning refers to a method used to adapt pre-trained models for a specific task by adjusting only the weights of its final few layers—the bottleneck layers—based on a newly available dataset. This approach allows us to retain and enhance key features from previously trained models while improving their performance on new tasks. During fine-tuning, we maintain fixed values for all earlier layers in the network, with only minor adjustments made to certain output layer(s) that directly contribute to achieving outcomes specific to current objectives. It is crucial to determine an optimal number of layers for fine-tuning considering factors such as model magnitude, complexity level required by tasks being addressed, and computational resources available. After experimenting with various configurations and parameter settings, selecting an architecture that balances performance efficiency is essential.

2.4 TensorFlow Hub

TensorFlow Hub是一个用于Tensorflow中的迁移学习库和平台。
它提供了一个集中存储预训练模型的地方，
这些模型可以轻松集成到您的Tensorflow代码库中，
从而无需担心下载和维护预训练模型。
此外，
TF-Hub还提供了通过Python APIs或命令行界面访问预训练模型的功能，
使用户能够快速尝试不同的预训练模型以解决各种任务，
而无需自行编写代码。
此外，
TF-Hub内置了处理文本嵌入的支持功能，
并提供了现成的预建嵌入向量以供自然语言处理任务使用。

3.核心算法原理和具体操作步骤以及数学公式讲解

In this section, we will explore each of the three computer vision examples: image classification, object detection, and text embeddings. Each example will delve into the steps required to implement transfer learning using TensorFlow Hub, which includes providing sample code snippets and explanatory materials.

3.1 Image Classification Using Transfer Learning with TensorFlow Hub

Image classification relies on categorizing images into predefined categories or classes. Such as an image depicting a cat, dog, or bird, a convolutional neural network can predict class labels like 'cat', 'dog,' and 'bird'. Transfer learning enhances convolutional neural networks' performance in image classification tasks through the utilization of extensive pretrained models available via TensorFlow Hub. Following are the fundamental steps to execute:

Select a ready-trained model from TensorFlow Hub. Various ready-trained models are available such as ResNet、VGG、MobileNet、Inception、EfficientNet等；their selection depends on both the complexity and size of the training dataset. For example, consider this code snippet that demonstrates loading a MobileNetV2 model from TensorFlow Hub:

    import tensorflow_hub as hub

    
    module = hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4")
    
         
         
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Fix all intermediate layers of the pre-trained model, leaving only the final fully connected layer trainable. This necessity ensures that no additional layers can be added without risking model untrainability and reduced effectiveness. Consequently, only the weights of this final layer are updated during training, which serves as the classifier head in our model. We implement this by setting trainable=False for all intermediate layers using Keras API syntax as follows:

    # freeze all layers except the last one

    for layer in module.layers[:-1]:
    layer.trainable = False
    
         
         
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向模型中添加一个新的输出层。该输出层的神经元数量应与我们的数据集中的类别数一致。例如，在分类图片为两类'猫'和'dog'的情况下，我们可以定义一个包含两个神经元的全连接层如下所示：

    import keras

    from keras.models import Model
    
    x = keras.layers.Dense(module.output_shape[1], activation='relu')(module.output)
    predictions = keras.layers.Dense(num_classes, activation='softmax')(x)
    
    model = Model(inputs=module.input, outputs=predictions)
    
         
         
         
         
         
         
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The model is trained on a limited dataset. Due to the fact that only the weights of the final fully connected layer are being updated, it suffices to provide corresponding labels and minimize the categorical cross-entropy loss function. Specifically, in cases where binary classification is being addressed, our custom loss function can be defined as follows:

    def weighted_binary_crossentropy(y_true, y_pred):

       pos_weight = sum(1 - y_true)/sum(y_true)
       return tf.nn.weighted_cross_entropy_with_logits(labels=y_true, logits=y_pred, pos_weight=pos_weight)
    
    model.compile(optimizer='adam', loss=weighted_binary_crossentropy, metrics=['accuracy'])
    history = model.fit(...)
    
         
         
         
         
         
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The comprehensive code for implementing transfer learning in image classification tasks is provided below.

    import tensorflow_hub as hub
    from keras.models import Sequential
    from keras.layers import Dense, Flatten, InputLayer
    from keras.applications.resnet50 import preprocess_input
    from sklearn.metrics import log_loss, confusion_matrix
    
    # Load MobileNetV2 model from TensorFlow Hub
    module = hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4")
    
    # Freeze all layers except the last one
    for layer in module.layers[:-1]:
    layer.trainable = False
    
    # Define a custom top layer
    model = Sequential()
    model.add(InputLayer(input_tensor=module.output))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))
    
    # Compile the model
    model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
    
    # Train the model
    history = model.fit(...,
                    validation_data=(val_images, val_labels),
                    epochs=EPOCHS, 
                    batch_size=BATCH_SIZE)
    
    # Evaluate the model
    preds = np.round(model.predict(test_images)).astype(int).flatten()
    score = log_loss(test_labels, preds)
    cm = confusion_matrix(test_labels, preds)
    print('Log Loss:', score)
    print('Confusion Matrix:\n', cm)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

Here, we imported a pre-trained MobileNetV2 model from TensorFlow Hub and froze all its layers except for the final one. We incorporated a new sigmoid output layer into the architecture. After compiling the model, it was trained on our small dataset. A custom loss function was created to assign higher weights to positive class examples compared to negative ones; this approach led to enhanced performance in our binary classification task. Following training, the model was evaluated on our test set to obtain log loss metrics and generate confusion matrices.

相应代码可以编写针对其他预训练模型和任务，并包括多类分类、回归分析以及语义分割等场景。只需要完成以下步骤：重新设计预处理步骤、自定义损失函数、评估指标以及批量大小设置。

3.2 Object Detection Using Transfer Learning with TensorFlow Hub

Object detection represents a method for identifying several objects within digital images by locating and classifying them. Transfer learning proves beneficial in scenarios where obtaining sufficient training data is challenging or when rapid and precise outcomes are desired. TensorFlow Hub offers a convenient platform for leveraging pre-trained models, which have the advantage of using vast amounts of data for training. These models include those trained on large datasets such as COCO (Common Objects in Context) or OpenImages. Following this guide will help you achieve success. Below, you'll find a concise overview of the essential steps involved.

Access a pre-trained model repository on TensorFlow Hub to obtain a desired pre-trained model. Choose between downloading an entire pre-trained model or accessing individual checkpoint files (e.g., .h5 format) for continued training or fine-tuning on your specific dataset.

Extract or retrieve the coordinates and labels of bounding boxes from downloaded checkpoints files or pre-trained models. This encompasses parsing outputs from SSD (Single Shot MultiBox Detector) formats used in object detection models, as well as extracting critical data points from meta-graph files associated with Faster RCNN models.

Generate annotations for the new dataset. Develop a JSON file containing accurate annotation structures for each detected object. For COCO datasets, we must create a list of dictionaries, each containing detailed information about detected objects, such as 'image_id', 'category_id', 'bbox', and optionally 'segmentation'.

4. Reformat annotations into an appropriate data structure for the pre-trained model. This process may require transforming a JSON file into a CSV format or altering an XML annotation file.

5. Optionally tune the configuration parameters of the pre-trained model. Some models are supplied with default configurations that perform well across a broad spectrum of tasks, though they may not be ideal for object detection involving small object dimensions or significant occlusion. If needed, consider adjusting the anchor boxes, stride settings, or additional hyperparameters to enhance performance.

Proceed with training or fine-tuning the pre-trained model on the new dataset by utilizing the adjusted configuration parameters. Consider either resuming training from where it left off or restoring from checkpoint files while adapting newly developed layers accordingly. Make sure that all anchors derived during prior training align with this revised input size. As an alternative approach, one could entirely disregard previous models and begin afresh with updated settings.

为了评估其准确率和速度性能，对该模型进行新图像上的测试。为避免光照条件、相机角度和缩放水平的变化导致的错误出现，在对图像进行处理前应确保截取图片，在通过模型前对其进行缩放，并将超出原始图像尺寸范围的预测边界框进行筛选处理。

The entire code block required for implementing transfer learning technique in object detection tasks is provided below.

    import tensorflow as tf
    import tensorflow_hub as hub
    
    # Load the pre-trained object detector from TensorFlow Hub
    detector = hub.load("https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1")
    
    # Specify the path to the local copy of the downloaded COCO annotations file
    annotations_path = '/path/to/annotations.json'
    
    # Parse the COCO annotations file to extract bbox coords and labels
    with open(annotations_path) as f:
      coco_annotations = json.load(f)
    
    bboxes = []
    categories = []
    for ann in coco_annotations['annotations']:
      xmin = ann['bbox'][0] / W
      ymin = ann['bbox'][1] / H
      w = ann['bbox'][2] / W
      h = ann['bbox'][3] / H
      category = ann['category_id']
      bboxes.append([xmin, ymin, w, h])
      categories.append(category)
    
    # Split the annotations into training and testing sets
    indices = np.arange(len(bboxes))
    np.random.shuffle(indices)
    split = int(len(bboxes)*0.8)
    train_indices = indices[:split]
    test_indices = indices[split:]
    train_bboxes = [bboxes[i] for i in train_indices]
    train_categories = [categories[i] for i in train_indices]
    test_bboxes = [bboxes[i] for i in test_indices]
    test_categories = [categories[i] for i in test_indices]
    
    # Resize the bboxes to fit the expected input shape of the pre-trained model
    train_resized_bboxes = tf.constant([[ymin, xmin, ymax, xmax]]*len(train_bboxes)) * [H, W, H, W]
    test_resized_bboxes = tf.constant([[ymin, xmin, ymax, xmax]]*len(test_bboxes)) * [H, W, H, W]
    
    # Run the object detector on the training and testing sets
    results = {}
    def run_detector(dataset_name, resized_bboxes, categories):
      decoded_images = tf.map_fn(lambda x: tf.io.decode_jpeg(tf.io.read_file(x)), img_paths, dtype=tf.uint8)
      scaled_images = tf.image.resize(decoded_images, (640, 640))/255.0
      scores, boxes, classes, num_detections = detector(scaled_images)
    
      rboxes = tf.squeeze(boxes)[...,:4].numpy().tolist()
      rscores = tf.squeeze(scores).numpy().tolist()
      rcats = tf.squeeze(classes).numpy().tolist()
    
      for i, img_path in enumerate(img_paths):
    basename = os.path.basename(os.path.splitext(img_path)[0]).replace('_','')
    gt_idx = next((j for j in range(len(coco_annotations['images'])) if coco_annotations['images'][j]['file_name']==basename), None)
    if gt_idx == None:
      print('{} not found in GT annotations.'.format(basename))
      continue
    
    pred_cats = [cls_dict[c]['id'] for cls_dict, c in zip(detector.get_num_classes(), rcats)]
    pred_bboxes = [(rbox/scale).tolist() for scale, rbox in zip([(H,W),(H,W),(H,W),(H,W)], rboxes)]
    
    if len(pred_cats)!=len(pred_bboxes):
      raise ValueError('Number of prediction categories does not match number of prediction bboxes.')
    
    results[(basename, dataset_name)] = {'gt':coco_annotations['annotations'][gt_idx],
                                       'pred':{'bboxes':pred_bboxes,
                                               'cats':pred_cats}}
    
      mAP = average_precision_score(test_categories, rscores)
      print('{} mAP: {:.4f}'.format(dataset_name, mAP))
    
    run_detector('train', train_resized_bboxes, train_categories)
    run_detector('test', test_resized_bboxes, test_categories)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

Here, we sourced the Inception-ResNet v2 Faster R-CNN model from TensorFlow Hub and parsed COCO annotations to obtain ground truth bounding boxes and predicted labels. The annotations were randomly partitioned into training and testing datasets, resizing bounding boxes to conform to pre-trained model input dimensions before running object detection on both sets. We then computed mAP scores separately for each dataset. In our study, we assumed a valid mapping between category IDs used by the pre-trained model and their corresponding semantic labels obtained externally.