Swin Transformer

Preprocess the ImageNet dataset.

Load the Swin Transformer model pre-trained on ImageNet-22K.

Fine-tune the model on the ImageNet-1K dataset.

Evaluate the model's accuracy on the validation set.

Report the top-1 and top-5 accuracy scores.

Prepare the COCO dataset.

Use Swin Transformer as the backbone for an object detection framework (e.g., Mask R-CNN).

Train the object detection model on the COCO training set.

Evaluate the model's performance on the COCO validation set using metrics like AP and mAP.

Visualize the detected bounding boxes and object categories.

Prepare the ADE20K dataset.

Use Swin Transformer as the backbone for a semantic segmentation model (e.g., UPerNet).

Train the segmentation model on the ADE20K training set.

Evaluate the model's performance on the ADE20K validation set using the mIoU metric.

Visualize the segmented regions with corresponding semantic labels.

Prepare the Kinetics dataset.

Use Video Swin Transformer to process video frames.

Train the video recognition model on the Kinetics training set.

Evaluate the model's performance on the Kinetics validation set using accuracy metrics.

Predict the action label for new video clips.

Apply SimMIM to pre-train Swin Transformer on a large unlabeled dataset.

Mask random patches in the input images.

Train the model to reconstruct the masked patches.

Transfer the pre-trained weights to downstream tasks like image classification and object detection.

Fine-tune the model on the target task using labeled data.