Contents
Can a ConvNet be trained on a small dataset?
Because convnets learn local, translation-invariant features, they’re highly data efficient on perceptual problems. Training a convnet from scratch on a very small image dataset will still yield reasonable results despite a relative lack of data, without the need for any custom feature engineering.
How to generate ConvNet training data in Blender?
Use Blender’s physics engine to randomly drop trash into the scene. Render the scene at many different camera angles. Save an image of each render. For each render, determine which objects are visible in the scene and their bounding boxes. Save these labels to a file.
Is it possible to train a ConvNet to solve a complex problem?
It isn’t possible to train a convnet to solve a complex problem with just a few tens of samples, but a few hundred can potentially suffice if the model is small and well regularized and the task is simple. Because convnets learn local, translation-invariant features, they’re highly data efficient on perceptual problems.
How to reuse the structure of a ConvNet?
You’ll reuse the same general structure: the convnet will be a stack of alternated Conv2D (with relu activation) and MaxPooling2D layers. But because you’re dealing with bigger images and a more complex problem, you’ll make your network larger, accordingly: it will have one more Conv2D + MaxPooling2D stage.
How to train deep transfer learning on small dataset?
Extract the CNN codes for all images, train a linear classifier (e.g. Linear SVM or Softmax classifier) for the new dataset. Fine-tuning the ConvNet.
How big of a dataset do you need to train image classification?
Having to train an image-classification model using very little data is a common situation, which you’ll likely encounter in practice if you ever do computer vision in a professional context. A “few” samples can mean anywhere from a few hundred to a few tens of thousands of images.
How to create cats and dogs small dataset?
After downloading and uncompressing it, you’ll create a new dataset containing three subsets: a training set with 1,000 samples of each class, a validation set with 500 samples of each class, and a test set with 500 samples of each class. After uncompressing data, Directory structure is below. (Before run below code, cats_and_dogs_small is empty)