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Questions and Answers
What is the total size of the Dogs vs. Cats dataset after uncompression?
What is the total size of the Dogs vs. Cats dataset after uncompression?
- 12,500 MB
- 500 MB
- 543 MB (correct)
- 25,000 MB
Which of the following is the first step in data preprocessing for the dataset?
Which of the following is the first step in data preprocessing for the dataset?
- Convert to floating-point tensors
- Rescale pixel values
- Read the picture files (correct)
- Decode the JPEG content
How many samples are included in the validation set for each class?
How many samples are included in the validation set for each class?
- 1,000
- 2,500
- 500 (correct)
- 25,000
What are the components of the dataset after it has been split?
What are the components of the dataset after it has been split?
What do neural networks prefer regarding input values during processing?
What do neural networks prefer regarding input values during processing?
From where can the Dogs vs. Cats dataset be downloaded?
From where can the Dogs vs. Cats dataset be downloaded?
What class in Keras assists with processing images into batches of tensors?
What class in Keras assists with processing images into batches of tensors?
What was the achieved test accuracy for the dataset?
What was the achieved test accuracy for the dataset?
What major advancement occurred in the top-5 error rate of the ImageNet Competition over a five-year span?
What major advancement occurred in the top-5 error rate of the ImageNet Competition over a five-year span?
What is the primary purpose of the LeNet-5 architecture?
What is the primary purpose of the LeNet-5 architecture?
Which technique was NOT used in AlexNet to reduce overfitting?
Which technique was NOT used in AlexNet to reduce overfitting?
How were MNIST images prepared before being fed into the LeNet-5 network?
How were MNIST images prepared before being fed into the LeNet-5 network?
What was a significant architectural difference between LeNet-5 and AlexNet?
What was a significant architectural difference between LeNet-5 and AlexNet?
What is a characteristic of the ImageNet dataset used in the competition?
What is a characteristic of the ImageNet dataset used in the competition?
Why is data augmentation important in the training of convolutional networks?
Why is data augmentation important in the training of convolutional networks?
Which of the following best describes the dropout technique used in AlexNet?
Which of the following best describes the dropout technique used in AlexNet?
What is the primary benefit of using a pretrained convnet?
What is the primary benefit of using a pretrained convnet?
What does feature extraction in convnets involve?
What does feature extraction in convnets involve?
Which dataset is commonly used to train models like VGG16?
Which dataset is commonly used to train models like VGG16?
What is the structure of a typical convnet for image classification?
What is the structure of a typical convnet for image classification?
How does one repurpose a pretrained network for a different task?
How does one repurpose a pretrained network for a different task?
What is the significance of having a diverse training dataset for a pretrained network?
What is the significance of having a diverse training dataset for a pretrained network?
What role does the VGG16 architecture play in deep learning?
What role does the VGG16 architecture play in deep learning?
What kind of improvement does 82% accuracy represent when compared to a non-regularized model with a 15% improvement?
What kind of improvement does 82% accuracy represent when compared to a non-regularized model with a 15% improvement?
What does freezing a layer in a model prevent during training?
What does freezing a layer in a model prevent during training?
What is the primary goal of feature extraction with data augmentation?
What is the primary goal of feature extraction with data augmentation?
What might affect a model's test accuracy according to the content?
What might affect a model's test accuracy according to the content?
What does fine-tuning a pretrained model involve?
What does fine-tuning a pretrained model involve?
What might be an explained reason for a modest improvement in test accuracy?
What might be an explained reason for a modest improvement in test accuracy?
What characterizes the dense classifier in the feature extraction process?
What characterizes the dense classifier in the feature extraction process?
Which of the following is not a stated purpose of data augmentation?
Which of the following is not a stated purpose of data augmentation?
Why might a model's accuracy on validation data be strong yet remain disappointing on test data?
Why might a model's accuracy on validation data be strong yet remain disappointing on test data?
What is the recommended approach when working with a small dataset and a convolutional base with a large number of parameters?
What is the recommended approach when working with a small dataset and a convolutional base with a large number of parameters?
What accuracy was achieved after fine-tuning the model mentioned?
What accuracy was achieved after fine-tuning the model mentioned?
Why is it considered unfair to compare the fine-tuning results of the given dataset with original competitors' results?
Why is it considered unfair to compare the fine-tuning results of the given dataset with original competitors' results?
How many samples were used for training in the example compared to the full dataset available during the competition?
How many samples were used for training in the example compared to the full dataset available during the competition?
What technique is mentioned as a method to overcome overfitting in small datasets?
What technique is mentioned as a method to overcome overfitting in small datasets?
Which of the following statements is true regarding pre-trained models?
Which of the following statements is true regarding pre-trained models?
What might be the impact of using regularization techniques?
What might be the impact of using regularization techniques?
What is implied by the phrase 'huge difference' in sample size during training?
What is implied by the phrase 'huge difference' in sample size during training?
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Study Notes
ImageNet Competition
- The top-5 error rate for image classification in the ImageNet Competition fell drastically from over 26% to barely over 3% in just five years.
- Top-5 error rate refers to the number of test images where the system's top 5 predictions did not include the correct answer.
- The images used in the competition were large (256 pixels high) and categorized into 1,000 classes, many with subtle distinctions.
- LeNet-5, created by Yann LeCun in 1998, is a widely known CNN architecture, originally used for handwritten digit recognition (MNIST).
Training a Convnet from Scratch
- The Dogs vs. Cats dataset, available on Kaggle, contains 25,000 images of dogs and cats (12,500 from each class).
- The dataset is divided into three subsets:
- Training set with 1,000 samples per class
- Validation set with 500 samples per class
- Test set with 500 samples per class
Data Preprocessing
- Data preprocessing involves reading image files, decoding JPEG content to RGB pixel grids, converting them to floating-point tensors, and rescaling pixel values to the [0, 1] interval.
- Keras provides the ImageDataGenerator class for automating image file processing into preprocessed tensors.
Data Augmentation
- Data augmentation significantly improves accuracy, achieving an 82% test accuracy, a 15% relative improvement over the non-regularized model.
Pre-trained Models
- A common approach to deep learning on small datasets is utilizing pre-trained models, networks trained on a large dataset with generic representations of the visual world.
- The VGG16 architecture, originally trained on ImageNet, is often used for feature extraction.
Fine-tuning Pre-trained Models
- Fine-tuning involves unfreezing the top layers of a frozen model base used for feature extraction and jointly training the newly added classifier along with these layers.
- This technique is called fine-tuning as it adjusts the deeper representations to make them more relevant to the specific problem.
- The best results were achieved with a test accuracy of 98.5%, demonstrating the value of pre-trained models and fine-tuning.
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