ssdlite320_mobilenet_v3_large

torchvision.models.detection.ssdlite320_mobilenet_v3_large(*, weights: Optional[SSDLite320_MobileNet_V3_Large_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, trainable_backbone_layers: Optional[int] = None, norm_layer: Optional[Callable[[...], Module]] = None, **kwargs: Any) → SSD

SSDlite model architecture with input size 320x320 and a MobileNetV3 Large backbone, as described at Searching for MobileNetV3 and MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Warning

The detection module is in Beta stage, and backward compatibility is not guaranteed.

See ssd300_vgg16() for more details.

Example

>>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(weights=SSDLite320_MobileNet_V3_Large_Weights.DEFAULT)
>>> model.eval()
>>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)]
>>> predictions = model(x)

Parameters:

• weights (SSDLite320_MobileNet_V3_Large_Weights, optional) – The pretrained weights to use. See SSDLite320_MobileNet_V3_Large_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 (MobileNet_V3_Large_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 6, with 6 meaning all backbone layers are trainable. If None is passed (the default) this value is set to 6.

• norm_layer (callable, optional) – Module specifying the normalization layer to use.

• **kwargs – parameters passed to the torchvision.models.detection.ssd.SSD base class. Please refer to the source code for more details about this class.

class torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights(value)

The model builder above accepts the following values as the weights parameter. SSDLite320_MobileNet_V3_Large_Weights.DEFAULT is equivalent to SSDLite320_MobileNet_V3_Large_Weights.COCO_V1. You can also use strings, e.g. weights='DEFAULT' or weights='COCO_V1'.

SSDLite320_MobileNet_V3_Large_Weights.COCO_V1:

These weights were produced by following a similar training recipe as on the paper. Also available as SSDLite320_MobileNet_V3_Large_Weights.DEFAULT.

box_map (on COCO-val2017)	21.3
num_params	3440060
categories	__background__, person, bicycle, … (88 omitted)
min_size	height=1, width=1
recipe	link
GFLOPS	0.58
File size	13.4 MB

The inference transforms are available at SSDLite320_MobileNet_V3_Large_Weights.COCO_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are rescaled to [0.0, 1.0].