Faster R-CNN Models

Construct Faster R-CNN model variants for object-detection task.

model_fasterrcnn_resnet50_fpn(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 91,
  score_thresh = 0.05,
  nms_thresh = 0.5,
  detections_per_img = 100,
  ...
)

model_fasterrcnn_resnet50_fpn_v2(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 91,
  score_thresh = 0.05,
  nms_thresh = 0.5,
  detections_per_img = 100,
  ...
)

model_fasterrcnn_mobilenet_v3_large_fpn(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 91,
  score_thresh = 0.05,
  nms_thresh = 0.5,
  detections_per_img = 100,
  ...
)

model_fasterrcnn_mobilenet_v3_large_320_fpn(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 91,
  score_thresh = 0.05,
  nms_thresh = 0.5,
  detections_per_img = 100,
  ...
)

Arguments

pretrained: Logical. If TRUE, loads pretrained weights from local file.
progress: Logical. Show progress bar during download (unused).
num_classes: Number of output classes (default: 91 for COCO).
score_thresh: Numeric. Minimum score threshold for detections (default: 0.05).
nms_thresh: Numeric. Non-Maximum Suppression (NMS) IoU threshold for removing overlapping boxes (default: 0.5).
detections_per_img: Integer. Maximum number of detections per image (default: 100).
...: Other arguments (unused).

Value

A fasterrcnn_model nn_module.

Functions

model_fasterrcnn_resnet50_fpn(): Faster R-CNN with ResNet-50 FPN
model_fasterrcnn_resnet50_fpn_v2(): Faster R-CNN with ResNet-50 FPN V2
model_fasterrcnn_mobilenet_v3_large_fpn(): Faster R-CNN with MobileNet V3 Large FPN
model_fasterrcnn_mobilenet_v3_large_320_fpn(): Faster R-CNN with MobileNet V3 Large 320 FPN

Task

Object detection over images with bounding boxes and class labels.

Input Format

Input images should be torch_tensors of shape (batch_size, 3, H, W) where H and W are typically around 800.

Available Models

model_fasterrcnn_resnet50_fpn()
model_fasterrcnn_resnet50_fpn_v2()
model_fasterrcnn_mobilenet_v3_large_fpn()
model_fasterrcnn_mobilenet_v3_large_320_fpn()