model_cons_params_en.md 43 KB
简体中文 | English
This document describes the construction parameters of each PaddleRS model trainer in detail, including their parameter names, parameter types, parameter descriptions and default values.
The BIT implementation based on PaddlePaddle.
The original article refers to H. Chen, et al., "Remote Sensing Image Change Detection With Transformers "(https://arxiv.org/abs/2103.00208).
This implementation adopts pretrained encoders, as opposed to the original work where weights are randomly initialized.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool, optional) |
Whether to use mixed loss function | False |
losses (list, optional) |
List of loss functions | None |
att_type (str, optional) |
Spatial attention type, optional values are 'CBAM' and 'BAM' |
'CBAM' |
ds_factor (int, optional) |
Downsampling factor | 1 |
backbone (str, optional) |
ResNet architecture to use as backbone. Currently only 'resnet18' and 'resnet34' are supported |
'resnet18' |
n_stages (int, optional) |
Number of ResNet stages used in the backbone, should be a value in {3, 4, 5} |
4 |
use_tokenizer (bool, optional) |
Whether to use tokenizer | True |
token_len (int, optional) |
Length of input token | 4 |
pool_mode (str, optional) |
Gets the pooling strategy for input tokens when 'use_tokenizer' is set to False. 'max' means global max pooling, 'avg' means global average pooling |
'max' |
pool_size (int, optional) |
When 'use_tokenizer' is set to False, the height and width of the pooled feature map |
2 |
enc_with_pos (bool, optional) |
Whether to add learned positional embeddings to the encoder's input feature sequence | True |
enc_depth (int, optional) |
Number of attention blocks used in encoder | 1 |
enc_head_dim (int, optional) |
Embedding dimension of each encoder head | 64 |
dec_depth (int, optional) |
Number of attention blocks used in decoder | 8 |
dec_head_dim (int, optional) |
Embedding dimension for each decoder head | 8 |
The CDNet implementation based on PaddlePaddle.
The original article refers to Pablo F. Alcantarilla, et al., "Street-View Change Detection with Deconvolut ional Networks"(https://link.springer.com/article/10.1007/s10514-018-9734-5).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 6 |
The ChangeFormer implementation based on PaddlePaddle.
The original article refers to Wele Gedara Chaminda Bandara, Vishal M. Patel, “A TRANSFORMER-BASED SIAMESE NETWORK FOR CHANGE DETECTION”(https://arxiv.org/pdf/2201.01293.pdf).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
decoder_softmax (bool) |
Whether to use softmax as the last layer activation function of the decoder | False |
embed_dim (int) |
Hidden layer dimension of the Transformer encoder | 256 |
The ChangeStar implementation with a FarSeg encoder based on PaddlePaddle.
The original article refers to Z. Zheng, et al., "Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery"(https://arxiv.org/abs/2108.07002).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
mid_channels (int) |
Number of channels in the middle layer of UNet | 256 |
inner_channels (int) |
Number of channels inside the attention module | 16 |
num_convs (int) |
Number of convolutional layers in UNet encoder and decoder | 4 |
scale_factor (float) |
Upsampling factor to scale the size of the output segmentation mask | 4.0 |
The DSAMNet implementation based on PaddlePaddle.
The original article refers to Q. Shi, et al., "A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection"(https://ieeexplore.ieee.org/document/9467555).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
ca_ratio (int) |
Channel compression ratio in channel attention module | 8 |
sa_kernel (int) |
Kernel size in the spatial attention module | 7 |
The DSIFN implementation based on PaddlePaddle.
The original article refers to C. Zhang, et al., "A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images"(https://www.sciencedirect.com/science/article/pii/S0924271620301532).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
use_dropout (bool) |
Whether to use dropout | False |
The FC-EF implementation based on PaddlePaddle.
The original article refers to Rodrigo Caye Daudt, et al. "Fully convolutional siamese networks for change detection"(https://arxiv.org/abs/1810.08462)`.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 6 |
use_dropout (bool) |
Whether to use dropout | False |
The FC-Siam-conc implementation based on PaddlePaddle.
The original article refers to Rodrigo Caye Daudt, et al. "Fully convolutional siamese networks for change detection"(https://arxiv.org/abs/1810.08462).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
use_dropout (bool) |
Whether to use dropout | False |
The FC-Siam-diff implementation based on PaddlePaddle.
The original article refers to Rodrigo Caye Daudt, et al. "Fully convolutional siamese networks for change detection"(https://arxiv.org/abs/1810.08462).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | int |
use_dropout (bool) |
Whether to use dropout | False |
The FCCDN implementation based on PaddlePaddle.
The original article refers to Pan Chen, et al., "FCCDN: Feature Constraint Network for VHR Image Change Detection"(https://arxiv.org/pdf/2105.10860.pdf).
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
The P2V-CD implementation based on PaddlePaddle.
The original article refers to M. Lin, et al. "Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images"(https://ieeexplore.ieee.org/document/9975266).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
video_len (int) |
Number of input video frames | 8 |
The SNUNet implementation based on PaddlePaddle.
The original article refers to S. Fang, et al., "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images" (https://ieeexplore.ieee.org/document/9355573).
| arg_name | Description | default |
|---|---|---|
in_channels (int) |
Number of channels of the input image | |
num_classes (int) |
Number of target classes | |
width (int, optional) |
Output channels of the first convolutional layer | 32 |
The STANet implementation based on PaddlePaddle.
The original article refers to H. Chen and Z. Shi, "A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection"(https://www.mdpi.com/2072-4292/12/10/1662).
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
width (int) |
Number of channels in the neural network | 32 |
The CondenseNetV2 implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
in_channels (int) |
Number of channels of the input image | 3 |
arch (str) |
Architecture of the model, can be 'A', 'B' or 'C' |
'A' |
The HRNet implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
The MobileNetV3 implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
The ResNet50-vd implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
The DRN implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
losses (list) |
List of loss functions | None |
sr_factor (int) |
Scaling factor for super-resolution, the size of the original image will be multiplied by this factor. For example, if the original image is H x W, the output image will be sr_factor * H x sr_factor * W. |
4 |
min_max (None \| tuple[float, float]) |
Minimum and maximum image pixel values | None |
scales (tuple[int]) |
Scaling factor | (2, 4) |
n_blocks (int) |
Number of residual blocks | 30 |
n_feats (int) |
Number of features in the residual block | 16 |
n_colors (int) |
Number of image channels | 3 |
rgb_range (float) |
Range of image pixel values | 1.0 |
negval (float) |
Negative value in nonlinear mapping | 0.2 |
Supplementary Description oflq_loss_weightparameter (float) |
Weight of the low-quality image loss, which is used to control the impact of the reconstruction loss on the overall loss of restoring the low-resolution input image into a high-resolution output image. | 0.1 |
dual_loss_weight (float) |
Weight of the bilateral loss | 0.1 |
The ESRGAN implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
losses (list) |
List of loss functions | None |
sr_factor (int) |
Scaling factor for super-resolution, the size of the original image will be multiplied by this factor. For example, if the original image is H x W, the output image will be sr_factor * H x sr_factor * W |
4 |
min_max (tuple) |
Minimum and maximum pixel values of the input image. If not specified, the data type's default minimum and maximum values are used | None |
use_gan (bool) |
Boolean indicating whether to use GAN (Generative Adversarial Network) during training. If yes, GAN will be used | True |
in_channels (int) |
Number of channels of the input image | 3 |
out_channels (int) |
Number of channels of the output image | 3 |
nf (int) |
Number of filters in the first convolutional layer of the model | 64 |
nb (int) |
Number of residual blocks in the model | 23 |
The LESRCNN implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
losses (list) |
List of loss functions | None |
sr_factor (int) |
Scaling factor for super-resolution, the size of the original image will be multiplied by this factor. For example, if the original image is H x W, the output image will be sr_factor * H x sr_factor * W. |
4 |
min_max (tuple) |
Minimum and maximum pixel values of the input image. If not specified, the data type's default minimum and maximum values are used. | None |
multi_scale (bool) |
Boolean indicating whether to train on multiple scales. If yes, multiple scales are used during training. | False |
group (int) |
Controls the number of groups for convolution operations. Standard convolution if set to 1, DWConv if set to the number of input channels. |
1 |
The Faster R-CNN implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 80 |
backbone (str) |
Backbone network model to use | 'ResNet50' |
with_fpn (bool) |
Boolean indicating whether to use Feature Pyramid Network (FPN) | True |
with_dcn (bool) |
Boolean indicating whether to use Deformable Convolutional Networks (DCN) | False |
aspect_ratios (list) |
List of aspect ratios of candidate boxes | [0.5, 1.0, 2.0] |
anchor_sizes (list) |
list of sizes of candidate boxes expressed as base sizes on each feature map | [[32], [64], [128], [256], [512]] |
keep_top_k (int) |
Number of predicted boxes to keep before NMS operation | 100 |
nms_threshold (float) |
Non-maximum suppression (NMS) threshold to use | 0.5 |
score_threshold (float) |
Score threshold for filtering predicted boxes | 0.05 |
fpn_num_channels (int) |
Number of channels for each pyramid layer in the FPN network | 256 |
rpn_batch_size_per_im (int) |
Ratio of positive and negative samples per image in the RPN network | 256 |
rpn_fg_fraction (float) |
Fraction of foreground samples in RPN network | 0.5 |
test_pre_nms_top_n (int) |
Number of predicted boxes to keep before NMS operation when testing. If not specified, keep_top_k is used. |
None |
test_post_nms_top_n (int) |
Number of predicted boxes to keep after NMS operation at test time | 1000 |
The PP-YOLO implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 80 |
backbone (str) |
PPYOLO's backbone network | 'ResNet50_vd_dcn' |
anchors (list[list[float]]) |
Size of predefined anchor boxes | None |
anchor_masks (list[list[int]]) |
Masks for predefined anchor boxes | None |
use_coord_conv (bool) |
Whether to use coordinate convolution | True |
use_iou_aware (bool) |
Whether to use IoU awareness | True |
use_spp (bool) |
Whether to use spatial pyramid pooling (SPP) | True |
use_drop_block (bool) |
Whether to use DropBlock regularization | True |
scale_x_y (float) |
Parameter to scale each predicted box | 1.05 |
ignore_threshold (float) |
IoU threshold used to assign predicted boxes to ground truth boxes | 0.7 |
label_smooth (bool) |
Whether to use label smoothing | False |
use_iou_loss (bool) |
Whether to use IoU Loss | True |
use_matrix_nms (bool) |
Whether to use Matrix NMS | True |
nms_score_threshold (float) |
NMS score threshold | 0.01 |
nms_topk (int) |
Maximum number of detections to keep before performing NMS | -1 |
nms_keep_topk (int) |
Maximum number of prediction boxes to keep after NMS | 100 |
nms_iou_threshold (float) |
NMS IoU threshold | 0.45 |
The PP-YOLO Tiny implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 80 |
backbone (str) |
Backbone network model name to use | 'MobileNetV3' |
anchors (list[list[float]]) |
List of anchor box sizes | [[10, 15], [24, 36], [72, 42], [35, 87], [102, 96] , [60, 170], [220, 125], [128, 222], [264, 266]] |
anchor_masks (list[list[int]]) |
Anchor box mask | [[6, 7, 8], [3, 4, 5], [0, 1, 2]] |
use_iou_aware (bool) |
Boolean value indicating whether to use IoU-aware loss | False |
use_spp (bool) |
Boolean indicating whether to use the SPP module | True |
use_drop_block (bool) |
Boolean value indicating whether to use the DropBlock block | True |
scale_x_y (float) |
Scaling parameter | 1.05 |
ignore_threshold (float) |
Ignore threshold | 0.5 |
label_smooth (bool) |
Boolean indicating whether to use label smoothing | False |
use_iou_loss (bool) |
Boolean value indicating whether to use IoU Loss | True |
use_matrix_nms (bool) |
Boolean indicating whether to use Matrix NMS | False |
nms_score_threshold (float) |
NMS score threshold | 0.005 |
nms_topk (int) |
Number of bounding boxes to keep before NMS operation | 1000 |
nms_keep_topk (int) |
Number of bounding boxes to keep after NMS operation | 100 |
nms_iou_threshold (float) |
NMS IoU threshold | 0.45 |
The PP-YOLOv2 implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 80 |
backbone (str) |
PPYOLO's backbone network | 'ResNet50_vd_dcn' |
anchors (list[list[float]]) |
Sizes of predefined anchor boxes | [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] |
anchor_masks (list[list[int]]) |
Masks of predefined anchor boxes | [[6, 7, 8], [3, 4, 5], [0, 1, 2]] |
use_iou_aware (bool) |
Whether to use IoU awareness | True |
use_spp (bool) |
Whether to use spatial pyramid pooling (SPP) | True |
use_drop_block (bool) |
Whether to use DropBlock regularization | True |
scale_x_y (float) |
Parameter to scale each predicted box | 1.05 |
ignore_threshold (float) |
IoU threshold used to assign predicted boxes to ground truth boxes | 0.7 |
label_smooth (bool) |
Whether to use label smoothing | False |
use_iou_loss (bool) |
Whether to use IoU Loss | True |
use_matrix_nms (bool) |
Whether to use Matrix NMS | True |
nms_score_threshold (float) |
NMS score threshold | 0.01 |
nms_topk (int) |
Maximum number of detections to keep before performing NMS | -1 |
nms_keep_topk (int) |
Maximum number of prediction boxes to keep after NMS | 100 |
nms_iou_threshold (float) |
NMS IoU threshold | 0.45 |
The YOLOv3 implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
num_classes (int) |
Number of target classes | 80 |
backbone (str) |
Name of the feature extraction network | 'MobileNetV1' |
anchors (list[list[int]]) |
Sizes of all anchor boxes | [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45 ], [59, 119], [116, 90], [156, 198], [373, 326]] |
anchor_masks (list[list[int]]) |
Which anchor boxes to use to predict the target box | [[6, 7, 8], [3, 4, 5], [0, 1, 2]] |
ignore_threshold (float) |
IoU threshold of the predicted box and the ground truth box, below which the threshold will be considered as the background | 0.7 |
nms_score_threshold (float) |
In non-maximum suppression, score threshold below which boxes will be discarded | 0.01 |
nms_topk (int) |
In non-maximum value suppression, the maximum number of scoring boxes to keep, if it is -1, all boxes are kept | 1000 |
nms_keep_topk (int) |
In non-maximum value suppression, the maximum number of boxes to keep per image | 100 |
nms_iou_threshold (float) |
In non-maximum value suppression, IoU threshold, boxes larger than this threshold will be discarded | 0.45 |
label_smooth (bool) |
Whether to use label smoothing when computing loss | False |
The BiSeNet V2 implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | {} |
align_corners (bool) |
Whether to use the corner alignment method | False |
The DeepLab V3+ implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
backbone (str) |
Backbone network type of neural network | ResNet50_vd |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
output_stride (int) |
Downsampling ratio of the output feature map relative to the input feature map | 8 |
backbone_indices (tuple) |
Output the location indices of different stages of the backbone network | (0, 3) |
aspp_ratios (tuple) |
Dilation ratio of dilated convolution | (1, 12, 24, 36) |
aspp_out_channels (int) |
Number of ASPP module output channels | 256 |
align_corners (bool) |
Whether to use the corner alignment method | False |
The FactSeg implementation based on PaddlePaddle.
The original article refers to A. Ma, J. Wang, Y. Zhong and Z. Zheng, "FactSeg: Foreground Activation -Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery,"in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5606216.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
The FarSeg implementation based on PaddlePaddle.
The original article refers to Zheng Z, Zhong Y, Wang J, et al. Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 4096-4105.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
The Fast-SCNN implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
align_corners (bool) |
Whether to use the corner alignment method | False |
The HRNet implementation based on PaddlePaddle.
| Parameter Name | Description | Default Value |
|---|---|---|
in_channels (int) |
Number of channels of the input image | 3 |
num_classes (int) |
Number of target classes | 2 |
width (int) |
Initial number of channels for the network | 48 |
use_mixed_loss (bool) |
Whether to use mixed loss function | False |
losses (list) |
List of loss functions | None |
align_corners (bool) |
Whether to use the corner alignment method | False |