ConvNeXt

Prepare the custom image dataset.

Fine-tune a pre-trained ConvNeXt model on the custom dataset using the provided training scripts.

Evaluate the fine-tuned model on a validation set.

Deploy the model for image classification tasks.

Integrate a pre-trained ConvNeXt model into an object detection pipeline (e.g., using Mask R-CNN).

Fine-tune the model on a dataset of surveillance images.

Deploy the model on edge devices or servers for real-time object detection.

Set up alerts for detected objects.

Adapt a pre-trained ConvNeXt model for semantic segmentation using the provided code.

Train the model on a dataset of medical images with corresponding segmentation masks.

Evaluate the model on a validation set.

Use the model to segment new medical images for diagnostic purposes.

Pre-process the images using standard image processing techniques.

Train the model on pairs of low-quality and high-quality images.

Use the trained model to enhance new images.

Evaluate the performance of the enhanced images using standard image quality metrics.

Load a pre-trained VGG model to extract content and style features.

Combine the content features of the content image with the style features of the style image.

Use a ConvNeXt model to reconstruct the stylized image.

Evaluate the performance by visual inspection.