train a instance segmentation neural network in python

To train an instance segmentation neural network in Python, you can follow these steps:

Prepare your dataset: You need a dataset of images and masks that segment each instance in the image. The mask should contain a different color or ID for each instance.
Define your model architecture: You can use a pre-trained model like Mask R-CNN or build your own architecture using libraries like TensorFlow or PyTorch.
Compile your model: You need to compile your model by specifying the loss, optimizer, and metrics. The loss function should be specific to instance segmentation, such as Mask R-CNN loss.
Train your model: You can use Stochastic Gradient Descent or other optimization algorithms to train your model. You should also specify the number of epochs, batch size, and other hyperparameters.

Here is sample code using Mask R-CNN in Keras:


main.py
from mrcnn.config import Config
from mrcnn.model import MaskRCNN
from mrcnn.utils import Dataset

# Define configuration
class InstanceSegmentationConfig(Config):
  # Give the configuration a recognizable name
  NAME = "instance_segmentation_config"
  # Number of classes (background + kangaroo)
  NUM_CLASSES = 1 + 1
  # Number of training steps per epoch
  STEPS_PER_EPOCH = 131

# Define dataset
class InstanceSegmentationDataset(Dataset):
  # Load the dataset definitions
  def load_dataset(self, ...):
    ...
  # Extract masks
  def load_mask(self, image_id):
    ...
  # Load an image reference
  def image_reference(self, image_id):
    ...
    
# Prepare training and validation datasets
train_set = ...
val_set = ...

# Define model
model = MaskRCNN(mode='training', model_dir='./', config=InstanceSegmentationConfig())

# Train model
model.train(train_set, val_set, learning_rate=config.LEARNING_RATE, epochs=5, layers='all')

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Note that this is just sample code and you should adjust it to your specific needs and dataset.

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related categories

instance-segmentation