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Types & classes32 in github.com/amdegroot/ssd.pytorch

↓ 4 callersClassBaseTransform
data/__init__.py:37
↓ 3 callersClassCompose
Composes several augmentations together. Args: transforms (List[Transform]): list of transforms to compose. Example: >>> augme
utils/augmentations.py:36
↓ 3 callersClassVOCAnnotationTransform
Transforms a VOC annotation into a Tensor of bbox coords and label index Initilized with a dictionary lookup of classnames to indexes Argumen
data/voc0712.py:31
↓ 3 callersClassVOCDetection
VOC Detection Dataset Object input is image, target is annotation Arguments: root (string): filepath to VOCdevkit folder. im
data/voc0712.py:80
↓ 2 callersClassConvertColor
utils/augmentations.py:155
↓ 2 callersClassRandomContrast
utils/augmentations.py:170
↓ 2 callersClassSSDAugmentation
utils/augmentations.py:400
↓ 2 callersClassTimer
A simple timer.
eval.py:79
↓ 1 callersClassCOCOAnnotationTransform
Transforms a COCO annotation into a Tensor of bbox coords and label index Initilized with a dictionary lookup of classnames to indexes
data/coco.py:42
↓ 1 callersClassCOCODetection
`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset. Args: root (string): Root directory where images are d
data/coco.py:75
↓ 1 callersClassConvertFromInts
utils/augmentations.py:67
↓ 1 callersClassDetect
At test time, Detect is the final layer of SSD. Decode location preds, apply non-maximum suppression to location predictions based on conf sc
layers/functions/detection.py:7
↓ 1 callersClassExpand
utils/augmentations.py:312
↓ 1 callersClassL2Norm
layers/modules/l2norm.py:7
↓ 1 callersClassMultiBoxLoss
SSD Weighted Loss Function Compute Targets: 1) Produce Confidence Target Indices by matching ground truth boxes with (default)
layers/modules/multibox_loss.py:10
↓ 1 callersClassPhotometricDistort
utils/augmentations.py:376
↓ 1 callersClassPriorBox
Compute priorbox coordinates in center-offset form for each source feature map.
layers/functions/prior_box.py:7
↓ 1 callersClassRandomBrightness
utils/augmentations.py:185
↓ 1 callersClassRandomHue
utils/augmentations.py:128
↓ 1 callersClassRandomLightingNoise
utils/augmentations.py:141
↓ 1 callersClassRandomMirror
utils/augmentations.py:340
↓ 1 callersClassRandomSampleCrop
Crop Arguments: img (Image): the image being input during training boxes (Tensor): the original bounding boxes in pt form
utils/augmentations.py:208
↓ 1 callersClassRandomSaturation
utils/augmentations.py:114
↓ 1 callersClassResize
utils/augmentations.py:104
↓ 1 callersClassSSD
Single Shot Multibox Architecture The network is composed of a base VGG network followed by the added multibox conv layers. Each multibox lay
ssd.py:10
↓ 1 callersClassSubtractMeans
utils/augmentations.py:72
↓ 1 callersClassSwapChannels
Transforms a tensorized image by swapping the channels in the order specified in the swap tuple. Args: swaps (int triple): final orde
utils/augmentations.py:350
↓ 1 callersClassToAbsoluteCoords
utils/augmentations.py:82
↓ 1 callersClassToPercentCoords
utils/augmentations.py:93
ClassLambda
Applies a lambda as a transform.
utils/augmentations.py:56
ClassToCV2Image
utils/augmentations.py:198
ClassToTensor
utils/augmentations.py:203