View Source Bumblebee.Vision.ResNet (Bumblebee v0.5.3)
ResNet model family.
Architectures
:base- plain ResNet without any head on top:for_image_classification- ResNet with a classification head. The head consists of a single dense layer on top of the pooled features and it returns logits corresponding to possible classes
Inputs
"pixel_values"- {batch_size, height, width, num_channels}Featurized image pixel values (224x224).
Configuration
:num_channels- the number of channels in the input. Defaults to3:embedding_size- the dimensionality of the embedding layer. Defaults to64:hidden_sizes- the dimensionality of hidden layers at each stage. Defaults to[256, 512, 1024, 2048]:depths- the depth (number of residual blocks) at each stage. Defaults to[3, 4, 6, 3]:residual_block_type- the residual block to use, either:basic(used for smaller models, like ResNet-18 or ResNet-34) or:bottleneck(used for larger models like ResNet-50 and above) . Defaults to:bottleneck:activation- the activation function. Defaults to:relu:downsample_in_first_stage- whether the first stage should downsample the inputs using a stride of 2. Defaults tofalse:output_hidden_states- whether the model should return all hidden states. Defaults tofalse:num_labels- the number of labels to use in the last layer for the classification task. Defaults to2:id_to_label- a map from class index to label. Defaults to%{}