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https://github.com/wdhorton/protein-atlas-fastai
Code for training a Resnet model for the Human Protein Atlas Image Classification competition
https://github.com/wdhorton/protein-atlas-fastai
Last synced: 3 months ago
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Code for training a Resnet model for the Human Protein Atlas Image Classification competition
- Host: GitHub
- URL: https://github.com/wdhorton/protein-atlas-fastai
- Owner: wdhorton
- Created: 2018-11-09T13:58:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-20T03:15:19.000Z (almost 6 years ago)
- Last Synced: 2024-07-03T04:04:15.417Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 1.48 MB
- Stars: 50
- Watchers: 4
- Forks: 12
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-fastai - Protein Atlas fastai
- awesome-fastai - Protein Atlas fastai
README
# Human Protein Atlas Image Classification Starter Pack
This is the code you need to train a resnet model and submit to the Human Protein Atlas competition.
It uses the newest version (v1) of the [fastai library](https://github.com/fastai/fastai).To download the data, I used the [Official Kaggle API package](https://github.com/Kaggle/kaggle-api), which you can install with pip. Once installed, you can run `kaggle competitions download -c human-protein-atlas-image-classification` to get the data (just make sure to update the `path` variable in the `resnet50_basic.ipynb` notebook to point to the data on your machine).
Update 11/17 -- the resnet50_basic notebook doesn't work with fastai version 1.0.25 and above, so I made another notebook to work with the new `data_block` API. In this version, I also made changes to use the `create_cnn` function. You can find it at resnet50_basic_datablocks.ipynb.