Vision Transformer with support for patch or hybrid CNN input stage

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

COCO evaluation metric. Evaluate AR, AP, and mAP for detection tasks including proposal/box detection and instance segmentation. Please refer

DOTA evaluation metric. Note: In addition to format the output results to JSON like CocoMetric, it can also generate the full image's result

IoU evaluation metric. Args: ignore_index (int): Index that will be ignored in evaluation. Default: 255. iou_metrics

r""" InternImage A PyTorch impl of : `InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions` - h

Compose multiple transforms sequentially. Args: transforms (Sequence[dict, callable], optional): Sequence of transform object

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

r""" Cross Attention Module Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 8

Data Parallel with a replication callback. An replication callback `__data_parallel_replicate__` of each module will be invoked after being

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

r""" Downsample layer of InternImage Args: channels (int): number of input channels norm_layer (str): normalization layer

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

A thread-safe future implementation. Used only as one-to-one pipe.

CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim.

CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim.