fasterrcnn_resnet50_fpn¶
- torchvision.models.detection.fasterrcnn_resnet50_fpn(*, weights: Optional[FasterRCNN_ResNet50_FPN_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, trainable_backbone_layers: Optional[int] = None, **kwargs: Any) FasterRCNN [source]¶
Faster R-CNN model with a ResNet-50-FPN backbone from the Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks paper.
Warning
The detection module is in Beta stage, and backward compatibility is not guaranteed.
The input to the model is expected to be a list of tensors, each of shape
[C, H, W]
, one for each image, and should be in0-1
range. Different images can have different sizes.The behavior of the model changes depending if it is in training or evaluation mode.
During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:
boxes (
FloatTensor[N, 4]
): the ground-truth boxes in[x1, y1, x2, y2]
format, with0 <= x1 < x2 <= W
and0 <= y1 < y2 <= H
.labels (
Int64Tensor[N]
): the class label for each ground-truth box
The model returns a
Dict[Tensor]
during training, containing the classification and regression losses for both the RPN and the R-CNN.During inference, the model requires only the input tensors, and returns the post-processed predictions as a
List[Dict[Tensor]]
, one for each input image. The fields of theDict
are as follows, whereN
is the number of detections:boxes (
FloatTensor[N, 4]
): the predicted boxes in[x1, y1, x2, y2]
format, with0 <= x1 < x2 <= W
and0 <= y1 < y2 <= H
.labels (
Int64Tensor[N]
): the predicted labels for each detectionscores (
Tensor[N]
): the scores of each detection
For more details on the output, you may refer to Instance segmentation models.
Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.
Example:
>>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT) >>> # For training >>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4) >>> boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4] >>> labels = torch.randint(1, 91, (4, 11)) >>> images = list(image for image in images) >>> targets = [] >>> for i in range(len(images)): >>> d = {} >>> d['boxes'] = boxes[i] >>> d['labels'] = labels[i] >>> targets.append(d) >>> output = model(images, targets) >>> # For inference >>> model.eval() >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] >>> predictions = model(x) >>> >>> # optionally, if you want to export the model to ONNX: >>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)
- Parameters:
weights (
FasterRCNN_ResNet50_FPN_Weights
, optional) – The pretrained weights to use. SeeFasterRCNN_ResNet50_FPN_Weights
below for more details, and possible values. By default, no pre-trained weights are used.progress (bool, optional) – 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.faster_rcnn.FasterRCNN
base class. Please refer to the source code for more details about this class.
- class torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.FasterRCNN_ResNet50_FPN_Weights.DEFAULT
is equivalent toFasterRCNN_ResNet50_FPN_Weights.COCO_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='COCO_V1'
.FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
These weights were produced by following a similar training recipe as on the paper. Also available as
FasterRCNN_ResNet50_FPN_Weights.DEFAULT
.box_map (on COCO-val2017)
37.0
categories
__background__, person, bicycle, … (88 omitted)
min_size
height=1, width=1
num_params
41755286
recipe
_ops
134.38 giga floating-point operations per sec
_weight_size
159.743 MB (file size)
The inference transforms are available at
FasterRCNN_ResNet50_FPN_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]
.
Examples using
fasterrcnn_resnet50_fpn
:Repurposing masks into bounding boxes
Repurposing masks into bounding boxesVisualization utilities