Deep Learning Flower Identifier

PyTorch implementation of a Deep learning network to identify 102 different types of flowers (developed for the PyTorch Scholarship Challenge).

The used data set contains images of flowers from 102 different species divided in a training set and a validation set.The images can be downloaded here: https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip

In addition, the repository contains a utility for testing the performance of a model on the original flower dataset (http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz) or alternatively on a dataset obtained by downloading the first 10 resulting images from Google, querying it by the name of the flower categories. This allows to evaluate if the model is not affected by overfitting and if it is suitable for a real usecase.

TRAIN THE MODEL
TEST THE MODEL
PUBLISH THE MODEL RESULTS
PUBLISH YOUR MODEL RESULTS
THE TEST SET

TRAIN THE MODEL

To train the model you can use different environments, including:

Google Colaboratory / Online notebook
Local machine

Google Colaboratory / Online notebook

You can find here a basic flowers classifier implementation on a Google Colaboratory file: https://drive.google.com/file/d/13_QyF9sghEIrc8iz4j5hPk0ssDE7-ExY/view?usp=sharing.

This will allow you to train your model and save a checkpoint.

Local machine

$git clone https://github.com/GabrielePicco/deep-learning-flower-identifier
$cd deep-learning-flower-identifier

To train the model on the local computer, first install the dependencies:

$pip install -r requirements.txt

after which you can simply run the training:

python flower_classifier.py

The script will automatically download the necessary data sets.

TEST THE MODEL

The colaboratory file (https://drive.google.com/file/d/13_QyF9sghEIrc8iz4j5hPk0ssDE7-ExY/view?usp=sharing) already contains the code needed to test the model's performance.

To test a saved model, simply load it into memory and recall the function calc_accuracy:

from test_model_pytorch_facebook_challenge import calc_accuracy

model = load_model('classifier.pth')
calc_accuracy(model, input_image_size=224)

If you are using a notebook

!git clone https://github.com/GabrielePicco/deep-learning-flower-identifier
!pip install requests
!pip install airtable
import sys
sys.path.insert(0, 'deep-learning-flower-identifier')
from test_model_pytorch_facebook_challenge import calc_accuracy

model = load_your_model('classifier.pth')
calc_accuracy(model, input_image_size=224)

The accuracy will be calculated on a dataset, downloaded on runtime, containing images classified by category, downloaded from Google search results.

You can also use the function to calculate the model's performance on a custom directory.

usage:

calc_accuracy(model, input_image_size, use_google_testset=False, testset_path=None, batch_size=32,
              norm_mean=[0.485, 0.456, 0.406], norm_std=[0.229, 0.224, 0.225]))                        
"""
Calculate the mean accuracy of the model on the test test
:param model: the model
:param use_google_testset: If true use the testset derived from google image
:param testset_path: custom test set or if default google images dataset is downloaded
:param batch_size:
:param input_image_size:
:param norm_mean: normalizazition mean for RGB channel
:param norm_std: stardard deviation mean for RGB channel
:return: the mean accuracy
"""

PUBLISH THE MODEL RESULTS

For collaborative purposes you can publish the results of your model online, along with a series of (optional) information on your model's structure.

The public results are visible here: https://airtable.com/shrCs1LDFdBus0tMG

To publish the results you can simply call the method publish_evaluated_model

from test_model_pytorch_facebook_challenge import publish_evaluated_model
publish_evaluated_model(model, input_image_size=224,  username="@Slack.Username", model_name="VGG19", optim="Adam",
                        criteria="NLLLoss", scheduler="StepLR", epoch=10)

PUBLISH YOUR MODEL RESULTS

From colab / notebook

Add this code cell

!git clone https://github.com/GabrielePicco/deep-learning-flower-identifier
!pip install requests
!pip install airtable
import sys
sys.path.insert(0, 'deep-learning-flower-identifier')

from test_model_pytorch_facebook_challenge import publish_evaluated_model

model = load_your_model('classifier.pth')

publish_evaluated_model(model, input_image_size=224,  username="@Slack.Username", model_name="VGG19", optim="Adam",
                    criteria="NLLLoss", scheduler="StepLR", epoch=10)

From local machine

$git clone https://github.com/GabrielePicco/deep-learning-flower-identifier
$pip install requests
$pip install airtable

In your script import sys sys.path.insert(0, 'deep-learning-flower-identifier')

from test_model_pytorch_facebook_challenge import publish_evaluated_model
publish_evaluated_model(model, input_image_size=224,  username="@Slack.Username", model_name="VGG19", optim="Adam",
                    criteria="NLLLoss", scheduler="StepLR", epoch=10)

THE TEST SET

If you are just interested in the test set, you can download them here

Original test set: https://www.dropbox.com/s/da6ye9genbsdzbq/flower_data_original_test.zip?dl=1

Google test set: https://www.dropbox.com/s/3zmf1kq58o909rq/google_test_data.zip?dl=1

If you are using a notebook you can use it directly:

!wget -O flower_data_orginal_test.zip "https://www.dropbox.com/s/da6ye9genbsdzbq/flower_data_original_test.zip?dl=1"
!unzip flower_data_orginal_test.zip


!wget -O google_test_data.zip "https://www.dropbox.com/s/3zmf1kq58o909rq/google_test_data.zip?dl=1"
!unzip google_test_data.zip

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