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简体中文 | English

Tutorials - Model Training

Sample code using the PaddleRS training model is curated in this directory. The code provides automatic downloading of sample data, and uses GPU to train the model.

Sample Code Path Task Model
change_detection/bit.py Change Detection BIT
change_detection/cdnet.py Change Detection CDNet
change_detection/changeformer.py Change Detection ChangeFormer
change_detection/dsamnet.py Change Detection DSAMNet
change_detection/dsifn.py Change Detection DSIFN
change_detection/fc_ef.py Change Detection FC-EF
change_detection/fc_siam_conc.py Change Detection FC-Siam-conc
change_detection/fc_siam_diff.py Change Detection FC-Siam-diff
change_detection/fccdn.py Change Detection FCCDN
change_detection/p2v.py Change Detection P2V-CD
change_detection/snunet.py Change Detection SNUNet
change_detection/stanet.py Change Detection STANet
classification/condensenetv2.py Scene Classification CondenseNet V2
classification/hrnet.py Scene Classification HRNet
classification/mobilenetv3.py Scene Classification MobileNetV3
classification/resnet50_vd.py Scene Classification ResNet50-vd
image_restoration/drn.py Image Restoration DRN
image_restoration/esrgan.py Image Restoration ESRGAN
image_restoration/lesrcnn.py Image Restoration LESRCNN
object_detection/faster_rcnn.py Object Detection Faster R-CNN
object_detection/fcosr.py Object Detection FCOSR
object_detection/ppyolo.py Object Detection PP-YOLO
object_detection/ppyolo_tiny.py Object Detection PP-YOLO Tiny
object_detection/ppyolov2.py Object Detection PP-YOLOv2
object_detection/yolov3.py Object Detection YOLOv3
semantic_segmentation/bisenetv2.py Image Segmentation BiSeNet V2
semantic_segmentation/deeplabv3p.py Image Segmentation DeepLab V3+
semantic_segmentation/factseg.py Image Segmentation FactSeg
semantic_segmentation/farseg.py Image Segmentation FarSeg
semantic_segmentation/fast_scnn.py Image Segmentation Fast-SCNN
semantic_segmentation/hrnet.py Image Segmentation HRNet
semantic_segmentation/unet.py Image Segmentation UNet

Start Training

  • After PaddleRS is installed, run the following commands to launch training with a single GPU. The script will automatically download the training data. Take DeepLab V3+ image segmentation model as an example:
# Specifies the GPU device number to be used
export CUDA_VISIBLE_DEVICES=0
python tutorials/train/semantic_segmentation/deeplabv3p.py
  • If multiple GPUs are required for training, for example, two graphics cards, run the following command:
python -m paddle.distributed.launch --gpus 0,1 tutorials/train/semantic_segmentation/deeplabv3p.py

Visualize Training Metrics via VisualDL

Set the use_vdl argument passed to the train() method to True, and then the training log will be automatically saved in VisualDL format in a subdirectory named vdl_log under the directory specified by save_dir (a user-specified path) during the model training process. You can run the following command to start the VisualDL service and view the indicators and metrics. We also take DeepLab V3+ as an example:

# The specified port number is 8001
visualdl --logdir output/deeplabv3p/vdl_log --port 8001

Once the service is started, open https://0.0.0.0:8001 or https://localhost:8001 in your browser to access the VisualDL page.