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♻️ Refresh code to work with 2024 library versions #3

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merged 9 commits into from
Nov 3, 2024
Merged

♻️ Refresh code to work with 2024 library versions #3

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492ac24
:memo: Add installation instructions and update Binder link
weiji14 Nov 1, 2024
eb9e2eb
:arrow_up: Bump pytorch and other dependencies
weiji14 Nov 1, 2024
49c03f7
:alien: Update CTCoreUnet to handle newer pytorch* library versions
weiji14 Nov 1, 2024
88060c6
:arrow_up: Bump setup-miniconda from 2.1.1 to 3.0.4 and other Actions
weiji14 Nov 2, 2024
c6e5a9a
:passport_control: Remove sha256 hashsum in explicit lockfile
weiji14 Nov 2, 2024
cc61b57
:passport_control: Trust downloading UNet model from torch.hub.load
weiji14 Nov 2, 2024
4421d8f
:mute: Only log to tensorboard if activated
weiji14 Nov 3, 2024
21dbdb9
:mag: Add teaser img of sediment core with IRD clasts to main README.md
weiji14 Nov 3, 2024
2eaa4c0
:pushpin: Use CUDA build of pytorch instead of CPU build
weiji14 Nov 3, 2024
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🔍 Add teaser img of sediment core with IRD clasts to main README.md 
Include a teaser image of a sediment core with Ice-Rafted Debris (IRD) clasts highlighted in red to the main README.md file. Also fixed a small typo.
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weiji14 committed Nov 3, 2024
commit 21dbdb9646fdd9296fdea4ed4de023c2925d4c7a
4 changes: 3 additions & 1 deletion README.md
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Expand Up @@ -7,6 +7,8 @@ Neural network for classifying rock clasts in computed tomography (CT) scans of
[![Test CTCoreNet](https://github.com/weiji14/ctcorenet/actions/workflows/python-app.yml/badge.svg)](https://github.com/weiji14/ctcorenet/actions/workflows/python-app.yml)
![License](https://img.shields.io/github/license/weiji14/ctcorenet)

![CT scan of sediment core with Ice-Rafted Debris (IRD) clasts highlighted in red](https://dagshub.com/weiji14/ctcorenet/raw/5a59d8f3c7f2d7b9a7bae50f4362b95994c34972/data/train/RS15-LC42_42-188.5-218.5/label_viz.png)

# Getting started

## Quickstart
Expand Down Expand Up @@ -72,7 +74,7 @@ This will load the image data stored in `data/train`, perform the training
(minimize loss between img.png and label.png), and produce some outputs.

More advanced users can customize the training, e.g. to be more deterministic,
running for only x epochs, train on an CUDA GPU using 16-bit precision, etc, like so:
running for only x epochs, train on a CUDA GPU using 16-bit precision, etc, like so:

python ctcorenet/ctcoreunet.py --deterministic=True --max_epochs=3 --accelerator=gpu --devices=1 --precision=16

Expand Down