ssd300_vgg16¶
-
torchvision.models.detection.
ssd300_vgg16
(pretrained: bool = False, progress: bool = True, num_classes: int = 91, pretrained_backbone: bool = True, trainable_backbone_layers: Optional[int] = None, **kwargs: Any)[source]¶ Constructs an SSD model with input size 300x300 and a VGG16 backbone.
Reference: “SSD: Single Shot MultiBox Detector”.
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 in 0-1 range. Different images can have different sizes but they will be resized to a fixed size before passing it to the backbone.
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.
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 the Dict are as follows, where
N
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 detection
scores (Tensor[N]): the scores for each detection
Example
>>> model = torchvision.models.detection.ssd300_vgg16(pretrained=True) >>> model.eval() >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)] >>> predictions = model(x)
- Parameters
pretrained (bool) – If True, returns a model pre-trained on COCO train2017
progress (bool) – If True, displays a progress bar of the download to stderr
num_classes (int) – number of output classes of the model (including the background)
pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int) – number of trainable (not frozen) resnet 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 4.
Examples using
ssd300_vgg16
: