The goal of this project is to predict the categories and attributes of the clothes. There are 46 categories and 1000 attributesin total. Category training and prediction has been completed.
└── deep-fashion-classification
├── main.py
├── inference.py
├── model.py
├── requirements.txt
├── README.md
└── data
├── category.csv
├── ...
└── models
├── category.h5
├── ...
└── samples
└── pred
├── sample_pred0.png
├── ...
├── blouse_cat3.jpg
├── coat_category39.jpg
├── ...
The locations of the files in the project are stated above.
To predict with the trained model:
python3 main.py --predict --predict-type categories
there are 6 predict types in total:
'categories'
'attribute1' (Textures)
'attribute2' (Fabrics)
'attribute3' (Shapes)
'attribute4' (Parts)
'attribute5' (Styles)
Will be added after attributes !!
- Category:
- Input Shape: (224, 224, 3) RGB
- Total params: 11,210,487
- Trainable params: 7,240,497
- Non-trainable params: 3,969,990
- Total layer number: 89
Whole model is trained on Google Colab. Takes approximately 16 hours for categories.
Library requirements are mentioned in requirements.txt
Required large file links below:
- Training images: https://drive.google.com/file/d/1oMbEqVW16nlxXGLx8TeeQ-evrZMJHcVF/view?usp=sharing
- CSV file for dataset: https://drive.google.com/file/d/19jP57kJ3pI-PkDlXdwQLAV6WvPM2P-z5/view?usp=sharing
- Category Labels: https://drive.google.com/file/d/1go3SOylcSNrX-ZRf6C9Ld0P2FjQ5Ut_K/view?usp=sharing
- Category Model File: https://drive.google.com/file/d/1r8zJ9XotrjpXvlBgbQbpNAHaCJTPUvjY/view?usp=sharing