PaddleSeg

PaddleSeg is a semantic segmentation algorithm library based on Paddle that supports many downstream tasks related to semantic segmentation.

This section shows how to use mmeval.MeanIoU for evaluation in PaddleSeg, and the related code can be found at mmeval/examples/paddleseg.

First you need to install Paddle and PaddleSeg, you can refer to the installation documentation in PaddleSeg. In addition, you need to download the pre-trained model to be evaluated, and prepare the evaluation data according to the configuration.

Scripts for model evaluation are provided in the PaddleSeg repo, and the model can be evaluated with the following commands:

python val.py --config <config_path> --model_path <model_path>

Note that the val.py script in the PaddleSeg only supports single-GPU evaluation, not multi-GPU evaluation yet.

MMEval provides a evaluation tools for PaddleSeg that use mmeval.MeanIoU, which can be executed with the following command:

# run evaluation
python ppseg_mmeval.py --config <config_path> --model_path <model_path>

# run evaluation with multi-gpus
python ppseg_mmeval.py --config <config_path> --model_path <model_path> --launcher paddle --num_process <num_gpus>

We tested this evaluation script on fastfcn_resnet50_os8_ade20k_480x480_120k and got the same evaluation results as the val.py in PaddleSeg.

Config	Weights	mIoU	aAcc	Kappa	mDice
fastfcn_resnet50_os8_ade20k_480x480_120k	model.pdparams	0.4373	0.8074	0.7928	0.5772