Kaggle plant seedling identification challenge
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data
scripts
seeds
.gitignore
LICENSE
README.md
requirements.txt

README.md

seeds

Seed identification Kaggle challenge.

This training and inference script is best described via its help dialog and looking at the source code.

$ git clone git@github.com:kahnvex/seeds.git && cd seeds/
$ pip install -r requirements.txt
$ python3 -m seeds.seeds --help
usage: seeds.py [-h] --name NAME [--epochs EPOCHS] [--batch-size BATCH_SIZE]
                [--learning-rate LEARNING_RATE] [--save-path SAVE_PATH]
                [--base-model {xception,resnet,densenet121,densenet169}]
                [--test] [--load-model] [--img-size IMG_SIZE]
                [--outfile OUTFILE]
                [--optimizer {adam,rmsprop,adagrad,adadelta}] [--pdist]
                [--ensemble ENSEMBLE [ENSEMBLE ...]]

optional arguments:
  -h, --help            show this help message and exit
  --name NAME           Name of the model
  --epochs EPOCHS, -e EPOCHS
  --batch-size BATCH_SIZE, -b BATCH_SIZE
  --learning-rate LEARNING_RATE, -l LEARNING_RATE
  --save-path SAVE_PATH
                        Path in which to save the model's h5 file
  --base-model {xception,resnet,densenet121,densenet169}
                        Architecture hyperparameter
  --test                Classify the test set
  --load-model          Load the model from disk instead of training
  --img-size IMG_SIZE   Input image size
  --outfile OUTFILE     Name of file for results
  --optimizer {adam,rmsprop,adagrad,adadelta}
                        The optimization function to use
  --pdist               Output the probability distribution as a CSV
  --ensemble ENSEMBLE [ENSEMBLE ...]
                        When testing, ensembe 1+ models using mean

You can see the Kaggle seed identification challenge here.

As of the time of this writing, several models trained by this script have scored in the top 5% of the Kaggle Seedling identification challenge.