Let us see another example of labeling images to classify dog or cat images, based on rules associated with properties.
The following are some rules to implement as LFs to detect images of dogs, based on pointy ears and snouts, the shape of the eyes, fur texture, and the shape of the body, as well as additional LFs to detect other features. The complete code for these functions is available on GitHub.
Labeling function 1: The rule is, if the image has pointy ears and a snout, label it as a dog:
# Define a labeling function to detect dogs based on pointy ears and snouts
def dog_features(image):
….
# If the image has pointy ears and a snout, label it as a dog
if has_pointy_ears and has_snout:
return 1
else:
return 0
Labeling function 2: The rule is, if the image has oval-shaped eyes, label it as a cat:
# Define a labeling function to detect cats based on their eyes
def cat_features(image):
# Label images as positive if they contain cat features #such as oval-shaped eyes
# If the image has oval-shaped eyes, label it as a cat
if has_oval_eyes:
return 1
else:
return 0
Labeling function 3: The rule is, if the image has a texture with high variance, label it as a dog:
# Define a labeling function to detect dogs based on fur texture
def dog_fur_texture(image):
# If the image has high variance, label it as a dog
if variance > 100:
return 1
else:
return 0
Labeling function 4: The rule is, if the aspect ratio is close to 1 (indicating a more circular shape), label it as a cat:
# Define a labeling function to detect cats based on their body shape
def cat_body_shape(image):
…..
# If the aspect ratio is close to 1 (indicating a more circular shape), label it as a cat
if abs(aspect_ratio – 1) < 0.1:
return 1
else:
return 0
The dog_features LF looks for the presence of pointy ears and snouts in the image by examining specific regions of the blue channel. The cat_features LF looks for the presence of oval-shaped eyes in the green channel. The dog_fur_texture LF looks for high variance in the grayscale version of the image, which is often associated with dog fur texture. The cat_body_shape LF looks for a circular body shape in the image, which is often associated with cats.
These LFs could be combined with Snorkel to create a model and label the images. In the next section, let us see how we can apply labels using transfer learning.
Labeling images using transfer learning
Transfer learning is a machine learning technique where a model trained on one task is adapted for a second related task. Instead of starting the learning process from scratch, transfer learning leverages knowledge gained from solving one problem and applies it to a different but related problem. This approach has become increasingly popular in deep learning and has several advantages:
- Faster training: Transfer learning can significantly reduce the time and computational resources required to train a model. Instead of training a deep neural network from random initialization, you start with a pre-trained model, which already has learned features and representations.
- Better generalization: Models pre-trained on large datasets, such as ImageNet for image recognition, have learned general features that are useful for various related tasks. These features tend to generalize well to new tasks, leading to better performance.
- Lower data requirements: Transfer learning can be especially beneficial when you have a limited amount of data for your target task. Pre-trained models can provide a head start, enabling effective learning with smaller datasets.
- Domain adaptation: Transfer learning helps adapt models from one domain (e.g., natural images) to another (e.g., medical images). This is valuable when collecting data in the target domain is challenging.
Let us see an example of Python code to detect digits in handwritten MNIST images, using Snorkel LFs.