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@abhishek-gola abhishek-gola commented May 22, 2025

Added NAFNet quantized model for deblurring DNN sample.

Quantized model size: 88MB

@abhishek-gola abhishek-gola requested a review from fengyuentau May 22, 2025 08:32
@abhishek-gola abhishek-gola self-assigned this May 22, 2025
@abhishek-gola abhishek-gola added this to the 5.0 milestone May 22, 2025
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Since our plan has run out of bandwith, could you upload the model somewhere (gdrive?) so that I can test it on my side?

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@abhishek-gola Friendly reminder.

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abhishek-gola commented May 27, 2025

Hi @fengyuentau,
I've removed the model from Git LFS to avoid bandwidth limitations and uploaded it to Google Drive instead. Here's the download link:

url: "https://drive.google.com/uc?export=dowload&id=1ZLRhkpCekNruJZggVpBgSoCx3k7bJ-5v"
sha1: "7dabf3d4ede0770ef326afc4511f7e67a791286d"

And also updated the download link in main repo (in models.yml).

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Tested the model manually on my host and it works as expected 👍

@abhishek-gola Could you add back the model to this PR? It helps to track the model version.

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@fengyuentau Added the model again.

@fengyuentau fengyuentau merged commit 21f2e86 into opencv:main May 28, 2025
asmorkalov pushed a commit to opencv/opencv that referenced this pull request Jun 11, 2025
Added DNN based deblurring samples #27349

Corresponding pull request adding quantized onnx model to opencv_zoo: opencv/opencv_zoo#295

Model size: 88MB

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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