TorchVision Transforms

Load image dataset

Define a transformation pipeline with random rotations, crops, and flips

Apply the transformations to the training data

Train a CNN model on the augmented data

Evaluate the model on the validation set

Load image and bounding box data

Define a transformation pipeline that includes random resizing and horizontal flips

Apply the transformations to both images and bounding boxes

Train an object detection model (e.g., Faster R-CNN) on the transformed data

Evaluate the model on a test set

Load image and segmentation mask data

Define a transformation pipeline with random crops and color jitter

Apply the transformations to both images and masks

Train a segmentation model (e.g., U-Net) on the transformed data

Evaluate the model on a validation set

Load video data

Define a transformation pipeline with random temporal crops and frame jitter

Apply the transformations to the video frames

Train a video classification model (e.g., 3D CNN) on the transformed data

Evaluate the model on a test set

Load image and keypoint data

Define a transformation pipeline with random rotations and scaling

Apply the transformations to both images and keypoints

Train a pose estimation model (e.g., Stacked Hourglass) on the transformed data

Evaluate the model on a validation set