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In the CVPR2023 paper, "Toward Accurate Post-Training Quantization for Image Super Resolution",
Table 5 (Sec. 4.2) of the paper seems unfair.
- PAMS and FQSR are implemented on EDSR-baseline mode…
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The following are the results of official pre-trained model EDSR_Lx4 (https://drive.google.com/drive/folders/1rtJCHuOAEixB1OWmUVbbVm158vzC3kTt)
 root@autodl-container-6b4f11a552-34a21590:~/autodl-tmp/EDSR-PyTorch/src# python main.py --model EDSR --scale 2 --save edsr_x2 --n_resblocks 32 --n_feats 256 --res_sca…
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I tried to run
`CUDA_VISIBLE_DEVICES=0 python main.py --model EDSR --scale 2 --save edsr_x2 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --reset --ext sep_reset`
on a single Tesla V100, but the tr…
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It drops some errors and I can't seem to fix it, if you could tell me what changes sh…
ghost updated
3 years ago
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In the example(/sr/edsr. py) you provided, before converting to INN, the pixel shuffle structure of edsr was replaced with torch. nn.Upsample, meaning that the structure of the INN model and DNN model…
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Hi @colorjam ,
As per the paper, claimed model size is 1.518MB for EDSR, 0.631MB for 8bit EDSR, 0.484MB for 4-bit EDSR model but in you google drive shared models all 3 models are of 6MB size. Can …
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Thanks for sharing the code.
But unfortunately, the training result from setting in run.sh doesn't meet the results of the paper.
Training for 8bit EDSR x4 only reaches 31.45 for set 5, which is far…