Better training on TF 2.1 about nsfw_model HOT 19 CLOSED

Alright let me see how this goes. It looks like validation accuracy isn't really moving anymore. Once I'm done everything (I have to attempt a second type of model still) I'll come back with the results. I'm skeptical until I test it outside of TF.

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IMO, I'd love for your code to be sent as a PR back to the repo. You could then host the models for a short amount of time in a dropbox link and I'll upload them to our S3 and host them for everyone for free.

I plan on learning fast.ai and seeing if I can beat TF 2.1 and TF-Hub - all the models and code are useful to the community!

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Very cool! Did you make one?

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@GantMan It's still going, on epoch 5/10 now and reporting ~96% train acc and ~92% validation still. I pulled your data set off mega for this FYI so thought I'd give back by sharing the above. :) Thanks for posting that.

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Awesome! I'd love to host the resulting file so others can use it, if that's good for you! Link to your socials etc. of course!

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lol after all that I can't get the damn model out of TF 2. I can't freeze it, only use it as a SaveModel which is useless. There is a way to freeze in 2.x but it looks like there's a regression where training parameters from hub get baked in as constants. Tomorrow's another day I guess to look at it.

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Gonna close this out because TF2 is a trainwreck. No way to properly freeze hub trained models and no tools to get them out of the SavedModel format support TF2.x properly. Sorry for the let down. I'm going to try to reproduce the same thing (xfer learning plus fine turning) in 1.15. If I'm successful I'll open a PR.

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@GantMan I've successfully re-written the repo to use TF 2.1 and TF-Hub so we get all models for TF 2 that are on tfhub.dev. Training is simple, you just give the URL of the model type you want and let it go.

Anyway I want to modify my fork to keep the trained models with LFS in the repo but it looks like I can't enable that on forks. What do you think? I guess the question is, what's more of a pain? To keep uploading models on the release page manually, or getting LFS enabled?

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Alright I broke my fork with LFS stuff so I'll redo it and send a PR excluding models. I'll then send models separately. I'm training inception v3, mobilenet v2 large, resnet 50 v2 and NasnetA. Exporting them to tflite, frozen pb, saved model, keras model and tfjs.

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AFAIK you can post the models as releases. Would you do that instead?

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I'm unfamiliar with exactly what posting models as releases means. Do you mean just include datestamp in the URI so people know when it was pushed?

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You just create a release like this:

And then you drop the zipped models here:

Then you let Github host your files :)

Anyway I really wanted to run training through on all models I configured but something has come up that has put me out of time. Sorry about that. I'll submit the PR as-is.

Also I don't think we're ever going to break out of the ~93% zone without modifying the source data set. There's too much ambiguity between "sexy" and "porn" so they're constantly confused. If you merge those categories you'll see that you can break that ceiling.

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@GantMan Alright PR is open. Let me know.

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Fantastic work! I love the single file with everything.

I would love to throw in quantized models in that zip and call it a day if that works for you!

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I'll have to check the command line tool that comes with tfjs when you install with pip. It may have a quant option for fp16 option. I'm out for a while tho today.

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I believe it's a flag in the tfjs command. --quantization_bytes 1 I believe!

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Also, I have ~360K images with object bounding boxes defined for everything you'd want to detect in terms of nsfw (not hentai tho). If you know or figure out how to use the tf2 and tf hub api to fine tune an SSD or SSDLite model, please let me know.

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@GantMan I created a new PR and linked to the updated model package that now has a quant model inside as well

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