DeepDanbooru VS Keras

Okay, it turns out that someone has trained an image classifier on danbooru as a dataset.

First of all - DeepDanbooru is the exclusive project of KichangKim, who aimed to train models based on (variants of) the ResNet architecture to output Danbooru tags.

> pip install -r requirements.txt Like what does this mean, where do i put the files i got from https://github.com/KichangKim/DeepDanbooru ? The whole process is confusing me

For: https://github.com/KichangKim/DeepDanbooru I see the "Put your deepbooru release project folder here.txt" file so I "git clone" it into this folder, moved it up and so on but the button did not show and in the console are no errors. Also I restarted the UI after each moving of the project.

if not is_installed("deepdanbooru") and deepdanbooru: run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")

i think you could make an argument that training an art-generator model falls afoul of the "market for the original work" prong of fair use in a pretty severe way. but something like DeepDanbooru where the output is not the image but a descriptor of it would be a much easier sell as fair use.

DeepDanbooru is suprisingly good for tagging photos and drawings.

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.