ResNet (PyTorch)

Load pre-trained ResNet model

Preprocess the input image

Pass the image through the model

Obtain class probabilities using softmax

Identify the top predicted class.

Use ResNet as a feature extractor within an object detection pipeline (e.g., Faster R-CNN)

Fine-tune the object detection model on a specific dataset

Input an image to the model

Output bounding boxes and class labels for detected objects.

Use ResNet as a feature extractor in a segmentation network (e.g., U-Net)

Train the segmentation model on a labeled dataset

Input an image to the model

Output a segmentation map indicating the class of each pixel.

Use ResNet to extract feature embeddings from facial images

Compare embeddings to identify matches

Implement a threshold to determine identity confidence.

Fine-tune ResNet on a medical image dataset

Input a medical image

Output a diagnosis or highlight regions of interest.

Use ResNet to extract content and style features from images

Combine the features to create a styled image

Refine the output using optimization techniques.