This project will utilized pre-trained model: vgg11, vgg13, and vgg16 to classify different species of flowers. The model was trained on dataset comprises of 102 flowers category, in RGB color. The first step to do is experimentation, I used different pre-trained model configuration, until I obtained the best model. Loss in Training-Validation-Test set are: 2.183, 0.824, 0.9112 with Accuracy in Validation-Test set are: 0.780 and 0.751.
Training Validation Loss
Example of prediction result
The second step I did, was code refactoring. Wrote simplified code version in Python script, so we can train the model and do prediction through CLI.
train.pywill train a new network on a dataset and save the model as a checkpoint. It will print out training loss, validation loss, and validation accuracy as the network trains.predict.pyuses a trained network to predict the class for an input image. You'll to pass a single image/path/to/imageand return the flower name and class probability.
You can run it by following below step:
1. Clone this repository
git clone https://github.com/roissyahf/Flower-Species-Prediction.git
2. Install required packages
pip install requirements.txt
3. Run train.py in the CLI to train the model
python train.py data_directory
Options available are:
- Set directory to save checkpoints:
python train.py data_dir --save_dir save_directory - Choose architecture:
python train.py data_dir --arch "vgg13" - Set hyperparameters:
python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20 - Use GPU for training:
python train.py data_dir --gpu
4. Run python predict.py in the CLI to get prediction
python predict.py
Options available are:
- Return top K most likely classes:
python predict.py input checkpoint --top_k 3 - Use a mapping of categories to real names:
python predict.py input checkpoint --category_names cat_to_name.json - Use GPU for inference:
python predict.py input checkpoint --gpu