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rcan-tensorflow's Introduction

hey there


Hello! I'm Hyeongchan from Korea. I enjoy programming, working out, and bouldering.

  • Service many machine learning products in various domains, Audio & Speech, Vision, NLP, Recommendation Systems, Tabular, and LLM applications in many startups.
  • Kaggle 2x Expert. the highest competition rank is top 0.1%.
  • Here's my C.V. Feel free to contact me at the email [email protected] :)

previously...

  • 2023 - 2024 : Joined Sionic.AI. Built enterprise-grade LLM applications.
  • 2021 - 2023 : Joined Viva Republica (Toss). Developed many products like BNPL, CSS, OCR, NPS, CDP, and in-house products.
  • 2020 - 2021 : Joined Watcha. Developed Watcha recommendation system, and contributed to other products like WatchaPedia, in-house applications.
  • 2019 - 2020 : Joined Rainist (Banksalad). Developed a transaction classifier service to analyze the categories with low latency, high accuracy, and in real-time.
  • 2019 : Joined VoyagerX. Developed a speaker diarization product that automatically recognizes the contents of the meeting.
  • - 2019 : offensive security stuffs. Mainly researched and studied Linux kernel exploitation and reverse engineering.

Current Interests

  • Kaggle Challenges
  • Building something in Rust
  • Bouldering

📊 this week i spent my time on:

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I'm a Night 🦉

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🌃 Evening                4839 commits        █████████░░░░░░░░░░░░░░░░   35.94 % 
🌙 Night                  2014 commits        ████░░░░░░░░░░░░░░░░░░░░░   14.96 %

Last Updated on 14/07/2024 15:23:58 UTC

📈 my github stats

kozistr

rcan-tensorflow's People

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Forkers

sf9218 yaozhaoyz bruinxiong xu-shi wangh-allen trackingbird deshmukhps95 jungchae huangrong5820 kay-shaw gaimjkp deeplearning2020 ljblkr hehongjie codersadis tvanguard2018 zoonono wenxiang2333 benblackcake edmontdants han-lim iq-scm yuripulier

rcan-tensorflow's Issues

[image] generation failed

some kind of problems, model? or sth don't work at all...

I tried some tries for image scaling like scaling to [0,1], subtracting div2k_mean, etc.... (paper way, my way)

but all failed :( so it needs to be fixed in some way.

training failed

Hello, I use your code to train the model using DIV2K, but can not get the training result, the output of the network is black image, my training loss is like:

[+] 28 epochs 23000 steps loss : 148.37326050 PSNR : -43.0488 SSIM : -0.0124
[+] 28 epochs 23100 steps loss : 157.56805420 PSNR : -44.0248 SSIM : -0.0029
[+] 29 epochs 23200 steps loss : 150.22140503 PSNR : -43.8336 SSIM : -0.0069
[+] 29 epochs 23300 steps loss : 131.76765442 PSNR : -42.0612 SSIM : 0.0020
[+] 29 epochs 23400 steps loss : 137.01979065 PSNR : -42.6417 SSIM : -0.0033
[+] 29 epochs 23500 steps loss : 139.48718262 PSNR : -42.9347 SSIM : -0.0040
[+] 29 epochs 23600 steps loss : 140.33860779 PSNR : -43.1099 SSIM : -0.0073
[+] 29 epochs 23700 steps loss : 137.87838745 PSNR : -43.3312 SSIM : -0.0022
[+] 29 epochs 23800 steps loss : 145.82002258 PSNR : -43.3227 SSIM : -0.0037
[+] 29 epochs 23900 steps loss : 154.25614929 PSNR : -44.0416 SSIM : -0.0016
[+] 30 epochs 24000 steps loss : 149.05319214 PSNR : -43.9319 SSIM : -0.0016
[+] 30 epochs 24100 steps loss : 154.17941284 PSNR : -44.0751 SSIM : -0.0096
[+] 30 epochs 24200 steps loss : 137.07835388 PSNR : -43.2535 SSIM : -0.0014
[+] 30 epochs 24300 steps loss : 156.63449097 PSNR : -44.3344 SSIM : 0.0001
[+] 30 epochs 24400 steps loss : 135.80091858 PSNR : -42.8577 SSIM : -0.0162
[+] 30 epochs 24500 steps loss : 138.67384338 PSNR : -43.0114 SSIM : 0.0011
[+] 30 epochs 24600 steps loss : 151.06549072 PSNR : -43.6310 SSIM : -0.0081
[+] 30 epochs 24700 steps loss : 158.61445618 PSNR : -44.2536 SSIM : -0.0010
[+] 31 epochs 24800 steps loss : 144.03248596 PSNR : -43.3128 SSIM : 0.0001

[image] augmentation

In the paper, 90, 180, 270 degree rotations are used for image augmentation.

  • rotation

[model] implements RCAN model

There're lots of modules like CA, etc... So, I'm gonna implement them one by one :)

  • CA (Channel Attention)
  • RCAB (Residual Channel Attention Block)
  • RG (Residual Group)
  • RCAN (Residual Channel Attention Network, final)

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