zhang jiajin

@CreeperLin

@aswanthkrishna This is a known bug because the wrong model was loaded. We will update the version in the next week to fix this bug.

Vega supports user custome model pruning for ResNet TensorFlow and MindSpore platform models, but does not support user custom PyTorch model pruning. The pytorch model pruning supported by Vega is...

@greatlog Please adjust the configuration. After the modification, the performance can be greater than 25 dB. ```yaml dataset: train: batch_size: 64 test: batch_size: 64 trainer: n_epoch: 150 ```

@greatlog The data needs to be processed as follows: For HR, an image needs to be cropped into multiple sub-images whose pixels are 480 x 480, and for LR, an...

@vanessasidrim Can not use custom datasets directly. There are two options: 1. Implement your dataset class and register it into Vega. 2. Or convert your dataset to Coco format.

@vanessasidrim You just need to change the data format. Please attach run logs to help resolve this issue. `/logs/`

@vanessasidrim It is possible that the number of classification does not match the pre-trained model. Adjust the number of classifications to finetune and check whether the precision increases. ```yaml general:...

@vanessasidrim Please update the config, specify the head name: ```yaml model: pretrained_model_file: /VEGA/vega/examples/nas/sp_nas/cache/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth head: roi_heads #

My logs: Before change code: ```text 2022-06-09 02:22:12.359 INFO ------------------------------------------------ 2022-06-09 02:22:12.359 INFO Step: fine_tune 2022-06-09 02:22:12.359 INFO ------------------------------------------------ 2022-06-09 02:22:12.366 INFO init TrainPipeStep... 2022-06-09 02:22:12.366 INFO TrainPipeStep started... 2022-06-09...