https://github.com/jamesbraza/cs330-project

Stanford CS330 Deep Multi-Task and Meta Learning Class Project
https://github.com/jamesbraza/cs330-project

course meta-learning stanford transfer-learning

Last synced: 2 days ago
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Stanford CS330 Deep Multi-Task and Meta Learning Class Project

• Host: GitHub
• URL: https://github.com/jamesbraza/cs330-project
• Owner: jamesbraza
• Created: 2022-11-10T02:26:56.000Z (about 2 years ago)
• Default Branch: develop
• Last Pushed: 2022-12-13T07:33:14.000Z (about 2 years ago)
• Last Synced: 2024-12-23T11:42:32.046Z (11 days ago)
• Topics: course, meta-learning, stanford, transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 10.5 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# cs330-project

[Stanford CS330][1]: Class Project.

TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset

## Datasets

We used a few datasets from Kaggle:

- [New Plant Diseases Dataset][2]:

  256 x 256 RGB JPG images of healthy and unhealthy crop leaves

  - Replaced with [TensorFlow Datasets `plant_village` dataset][5]

- [Plant Leaves for Image Classification][4]:

  6000 x 4000 RGB JPG images of healthy and unhealthy leaves from 12 plants

- [BIRDS 450 SPECIES- IMAGE CLASSIFICATION][3]:

  224 x 224 RGB JPG images of bird species

Here's how to easily download them all with the Kaggle API:

```bash

kaggle datasets download -p data/plant-diseases --unzip vipoooool/new-plant-diseases-dataset

kaggle datasets download -p data/plant-leaves --unzip csafrit2/plant-leaves-for-image-classification

kaggle datasets download -p data/bird-species --unzip gpiosenka/100-bird-species

```

## Developers

This project was developed using Python 3.10.

### Getting Started

Here is how to create a virtual environment to work with this repo:

```bash

python -m venv venv

source venv/bin/activate

python -m pip install -r requirements.txt

```

#### Including Code QA Tooling

We love quality code!  If you do too,

run these commands after creating the environment:

```bash

python -m pip install -r requirements-qa.txt

pre-commit install

```

### Debugging with `tensorboard`

Here is how you kick off `tensorboard`:

```bash

tensorboard --logdir training

```

Afterwards, go to its URL: [http://localhost:6006/](http://localhost:6006/).

[1]: https://cs330.stanford.edu/

[2]: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset

[3]: https://www.kaggle.com/datasets/gpiosenka/100-bird-species

[4]: https://www.kaggle.com/datasets/csafrit2/plant-leaves-for-image-classification

[5]: https://www.tensorflow.org/datasets/catalog/plant_village