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https://github.com/luminide/example-sorghum
Sorghum -100 Cultivar Identification - FGVC 9
https://github.com/luminide/example-sorghum
Last synced: 8 days ago
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Sorghum -100 Cultivar Identification - FGVC 9
- Host: GitHub
- URL: https://github.com/luminide/example-sorghum
- Owner: luminide
- Created: 2022-04-22T21:23:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-04-22T21:27:07.000Z (over 2 years ago)
- Last Synced: 2024-08-02T15:35:46.240Z (3 months ago)
- Language: Python
- Size: 13.7 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sorghum -100 Cultivar Identification - FGVC 9
### Introduction
This repository contains source code generated by [Luminide](https://luminide.com). It may be used to train, validate and tune deep learning models for image classification. The following directory structure is assumed:
```
├── code (source code)
├── input (dataset)
└── output (working directory)
```
The dataset should have images inside a directory named `train_images` and a CSV file named `train_cultivar_mapping.csv`. An example is shown below:
```
input
├── train_cultivar_mapping.csv
└── train_images
├── 2017-06-16__12-24-20-930_0.jpg
├── 2017-06-16__12-24-20-930_1.jpg
├── 2017-06-16__12-24-20-930_2.jpg
```
The CSV file is expected to have labels under a column named `cultivar` as in the example below:
```
image,cultivar
2017-06-16__12-24-20-930.jpg,PI_257599
2017-06-02__16-48-57-866.jpg,PI_154987
2017-06-12__13-18-07-707.jpg,PI_92270
```
### Quick recipe for using this repo with Luminide
- Accept [competition rules](https://www.kaggle.com/competitions/sorghum-id-fgvc-9/rules).
- Attach a Compute Server that has a GPU (e.g. gcp-t4).
- Configure your [Kaggle API token](https://github.com/Kaggle/kaggle-api) on the `Import Data` tab.
- On the `Import Data` tab, choose Kaggle and then enter `anlthms/sorghum1` (User Dataset).
- Train a model using the `Run Experiment` menu.
- Launch inference.sh from the `Run Experiment` tab to create a submission and use submit.sh to upload it to Kaggle.
- Check the [leaderboard](https://www.kaggle.com/competitions/sorghum-id-fgvc-9/leaderboard) to see your score!
### Additional features
- Use the `Experiment Tracking` menu to track experiments.
- To tune the hyperparameters, edit [sweep.yaml](sweep.yaml) as desired and launch a sweep from the `Run Experiment` tab. Tuned values will be copied back to a file called `config-tuned.yaml` along with visualizations in `sweep-results.html`.
- To use the tuned hyperparameter values, copy them over to `config.yaml` before training a model.
- For exploratory analysis, run [eda.ipynb](eda.ipynb).
- To monitor training progress, use the `Experiment Visualization` menu.
- After an experiment is complete, use the file browser on the IDE interface to access the results on the IDE Server.
- To generate a report on the most recent training session, run report.sh from the `Run Experiment` tab. Make sure `Track Experiment` is checked. The results will be copied back to a file called `report.html`.
**NOTE**: As configured, the code trains on 20% of the data. To train on the entire dataset, edit `full.sh` and `fast.sh` to remove the `--subset` command line parameter so that the default value of 100 is used.
For more details on usage, see [Luminide documentation](https://luminide.readthedocs.io)