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https://github.com/firaja/flowers-classification
Classification of flowers from Oxford Flowers 102 dataset
https://github.com/firaja/flowers-classification
advanced-machine-learning bicocca flower-classification image-classification keras machine-learning oxford-flower-dataset tensorflow
Last synced: 16 days ago
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Classification of flowers from Oxford Flowers 102 dataset
- Host: GitHub
- URL: https://github.com/firaja/flowers-classification
- Owner: firaja
- Created: 2022-07-22T16:06:20.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-23T14:25:41.000Z (about 1 year ago)
- Last Synced: 2023-08-23T16:08:25.967Z (about 1 year ago)
- Topics: advanced-machine-learning, bicocca, flower-classification, image-classification, keras, machine-learning, oxford-flower-dataset, tensorflow
- Language: TeX
- Homepage:
- Size: 33.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-ai-data-github-repos - Flowers Classification
README
# Classification of flower images starting from a small dataset
## Installation
Python3 is required alogn with cudnna and CUDA Toolkit.
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the required dependencies.
```bash
pip install -r requirements.txt
```
Download the dataset from https://www.robots.ox.ac.uk/~vgg/data/flowers/102/
Create a folder called `data` and move the downloaded archive. Then execute
```bash
matlab -nodisplay -nosplash -nodesktop -r "run('split_dataset_paper.m');exit;"
```
in the same folder.
## Usage
### Training
```bash
trainer.py [-h] [--batch [BATCH]] [--arch [ARCHITECTURE]] [--opt [OPTIMIZER]] [--clr [CLR]] [--step [STEP]] [--dropout [DROPOUT]] [--config [CONFIG]] [--mp] [--da] [--epoch [EPOCH]]
```
```
optional arguments:
-h, --help show this help message and exit
--batch [BATCH] Batch size used during training
--arch [{efficientnetb4,frozenefficientnetb4,inceptionv3,resnet18}]
Architecture
--opt [{Adam,SGD}] Optimizer
--clr [{triangular,triangular2,exp}]
Cyclical learning rate
--step [STEP] Step size
--dropout [DROPOUT] Dropout rate (when used with FrozenEfficientNetB4 it's used for the freeze rate)
--config [CONFIG] Configuration file
--mp Enable mixed precision operations (16bit-32bit)
--da Enable Data Augmentation
--epoch [EPOCH] Set the number of epochs
```
The script will produce plots and checkpoints in `./output/plots` and `./output/checkpoints`
### Learning Rate Finder
```bash
python learningratefinder.py [-h] [--batch [BATCH]] [--arch [ARCHITECTURE]] [--opt [OPTIMIZER]] [--dropout [DROPOUT]] [--config [CONFIG]] [--da] [--freeze [FREEZE]] [--epoch [EPOCH]]
```
```
optional arguments:
-h, --help show this help message and exit
--batch [BATCH] Batch size used during training
--arch [{efficientnetb4,frozenefficientnetb4,inceptionv3,resnet18}]
Architecture
--opt [{Adam,SGD}] Optimizer
--dropout [DROPOUT] Dropout rate
--config [CONFIG] Configuration file
--da Enable Data Augmentation
--freeze [FREEZE] Frozen layers
--epoch [EPOCH] Set the number of epochs
```
## Visualization
To visualize Saliency Map and Grad-CAM run
```bash
python3 visualize.py
```
The image must be store with the following pattern `./images/{class}/{image}.jpg`
## Contributing
The repository is hosted in Github https://github.com/firaja/aml
## License
[MIT](https://choosealicense.com/licenses/mit/)