The data folder consists of 2 folders and 3 CSV files:

• train - Contains 18540 images from 102 categories of flowers
• test - Contains 2009 images
• train.csv - Contains 2 columns and 18541 rows (including the headers), which consists of image id and the true label for each of the images in the train folder
• test.csv - Contains the image id for the images present in test folder for which the true label needs to be predicted sample_submission.csv - Specifies the format for the submission file

For more information on type of flowers provided as input you can visit here with valid hackerearth account. One can download the dataset from here.

Here are some of the sample images from the dataset:

The dependencies to run this code are:

• Numpy
• Pandas
• Pretrainedmodels
• PyTorch
• Fastai

Please follow notebooks to get information on the preprocessing done on images before training the model

Transfer learning is a technique to use a model learned on one problem to apply it to similar problem. We can efficiently achieve state-of-the-art results in short span of time. Special props to @rednivrug for providing the baseline. The final solution is an ensemble of three architectures of popular

1. ResNet
2. DenseNet

more information is given in the code

To train the model, you can run

./src/code_HE_Train.ipynb

Edit the path to load the dataset and save the weights

For just testing the model, run

./src/code_HE_Train.ipynb

Download the weights from the link given in the notebook and edit the path to load them

I was able to achieve rank of 17 out of 7620 users taking part in this competition with an accuracy of 89.99390 in just 5 days. This demonstrates the power of ensemble learning. One will have to re-train learn and learn2 models to get the final accuracy.

The dataset belongs to hackerearth and dataquest. It is used here to demonstrate model capabilities.