MaxViT Model

Implementation of the MaxViT architecture described in MaxViT: Multi-Axis Vision Transformer. The model performs image classification and by default returns logits for 1000 ImageNet classes.

model_maxvit(pretrained = FALSE, progress = TRUE, num_classes = 1000, ...)

Arguments

pretrained

(bool): If TRUE, returns a model pre-trained on ImageNet.

progress

(bool): If TRUE, displays a progress bar of the download to stderr.

num_classes

(integer) Number of output classes.

...

Additional parameters passed to the model initializer.

See also

Other classification_model: model_alexnet(), model_convnext, model_efficientnet, model_efficientnet_v2, model_inception_v3(), model_mobilenet_v2(), model_mobilenet_v3, model_resnet, model_vgg, model_vit

Examples

if (FALSE) { # \dontrun{
library(magrittr)
# 1. Load the basketball image
img_url <- "https://upload.wikimedia.org/wikipedia/commons/7/7a/Basketball.png"
img <- base_loader(img_url)

# 2. Define normalization (ImageNet)
norm_mean <- c(0.485, 0.456, 0.406)
norm_std <- c(0.229, 0.224, 0.225)

# 3. Preprocess: convert to tensor, resize, Normalize
input <- img %>%
  transform_to_tensor() %>%
  transform_resize(c(400, 400)) %>%
  transform_normalize(norm_mean, norm_std)
batch <- input$unsqueeze(1)    # Add batch dimension (1, 3, H, W)

# 4. Display the image before normalization
tensor_image_browse(input)

# 5. Load MaxViT model
model <- model_maxvit(pretrained = TRUE)
model$eval()

# 6. Run inference
output <- model(batch)
topk <- output$topk(k = 5, dim = 2)
indices <- as.integer(topk[[2]][1, ])
scores <- as.numeric(topk[[1]][1, ])

# 7. Show Top-5 predictions
glue::glue("{seq_along(indices)}. {imagenet_label(indices)} ({round(scores, 2)}%)")
} # }