25 lines
1.2 KiB
Markdown
Executable File
25 lines
1.2 KiB
Markdown
Executable File
# Tentative Title For A ResNet-Based Image Classifier
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This is a simple ResNet based image classifier for """specific images""", using a similar training framework I use to train [VALL-E](https://git.ecker.tech/mrq/vall-e/).
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## Training
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1. Throw the images you want to train under `./data/images/`.
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2. Modify the `./data/config.yaml` accordingly.
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3. Install using `pip3 install -e ./image_classifier/`.
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4. Train using `python3 -m image_classifier.train yaml='./data/config.yaml'`.
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5. Wait.
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## Inferencing
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Simply invoke the inferencer with the following command: `python3 -m image_classifier "./data/path-to-your-image.png" yaml="./data/config.yaml" --temp=1.0`
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## Caveats
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This was cobbled together in a night, partly to test how well my training framework fares when not married to my VALL-E implementation, and partly to solve a problem I have recently faced. Since I've been balls deep in learning the ins and outs of making VALL-E work, why not do the exact opposite (a tiny, image classification model of fixed lengths) to test the framework and my knowledge? Thus, this """ambiguous""" project is born.
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This is by no ways state of the art, as it just leverages an existing ResNet arch provided by `torchvision`. |