retinanet_resnet50_fpn_v2¶
- torchvision.models.detection.retinanet_resnet50_fpn_v2(*, weights: Optional[RetinaNet_ResNet50_FPN_V2_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[ResNet50_Weights] = None, trainable_backbone_layers: Optional[int] = None, **kwargs: Any) RetinaNet [source]¶
Constructs an improved RetinaNet model with a ResNet-50-FPN backbone.
Warning
The detection module is in Beta stage, and backward compatibility is not guaranteed.
Reference: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection.
retinanet_resnet50_fpn()
for more details.- Parameters:
weights (
RetinaNet_ResNet50_FPN_V2_Weights
, optional) – The pretrained weights to use. SeeRetinaNet_ResNet50_FPN_V2_Weights
below for more details, and possible values. By default, no pre-trained weights are used.progress (bool) – If True, displays a progress bar of the download to stderr. Default is True.
num_classes (int, optional) – number of output classes of the model (including the background)
weights_backbone (
ResNet50_Weights
, optional) – The pretrained weights for the backbone.trainable_backbone_layers (int, optional) – number of trainable (not frozen) layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If
None
is passed (the default) this value is set to 3.**kwargs – parameters passed to the
torchvision.models.detection.RetinaNet
base class. Please refer to the source code for more details about this class.
- class torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
is equivalent toRetinaNet_ResNet50_FPN_V2_Weights.COCO_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='COCO_V1'
.RetinaNet_ResNet50_FPN_V2_Weights.COCO_V1:
These weights were produced using an enhanced training recipe to boost the model accuracy. Also available as
RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
.box_map (on COCO-val2017)
41.5
categories
__background__, person, bicycle, … (88 omitted)
min_size
height=1, width=1
num_params
38198935
recipe
GFLOPS
152.24
File size
146.0 MB
The inference transforms are available at
RetinaNet_ResNet50_FPN_V2_Weights.COCO_V1.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are rescaled to[0.0, 1.0]
.