https://github.com/alvii147/smartwardrobe
Program that identifies pieces of clothing
https://github.com/alvii147/smartwardrobe
keras matplotlib pillow pyqt5 tensorflow
Last synced: about 2 months ago
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Program that identifies pieces of clothing
- Host: GitHub
- URL: https://github.com/alvii147/smartwardrobe
- Owner: alvii147
- Created: 2019-10-01T22:59:35.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-11-17T06:13:04.000Z (over 5 years ago)
- Last Synced: 2025-04-28T16:55:52.739Z (about 1 year ago)
- Topics: keras, matplotlib, pillow, pyqt5, tensorflow
- Language: Python
- Homepage:
- Size: 44.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Smart Wardrobe
## Overview
***Smart Wardrobe*** detects and categorizes clothing into one of 10 groups:
- T-Shirt/Top
- Trousers
- Pullover
- Dress
- Coat
- Sandals
- Shirt
- Sneakers
- Bag
- Ankle Boots
## Command Line Mode
Command line mode can be used to train the model, test the model using images in the img directory, or display the images in the img directory.
```
$ python SmartWardrobe.py
Enter command> help
COMMANDS
train train dataset
test predict images in img folder
display [FILE NAME] display image file
```
### Training
The model is trained using a **multi-layered neural network** in **Tensorflow** and **Keras.**
```
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(784, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(196, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation = tf.nn.softmax))
...
model.compile(optimizer = optimizer, loss = lossFunction, metrics = ["acc", "mse"])
...
history = model.fit(x_train, y_train, epochs = epochsCount)
```
Optimizing using **stochastic gradient descent,** using a **sparse categorical entropy** loss function and with 3 epochs the accuracy stands at roughly **73%.**
```
$ python SmartWardrobe.py
Enter command> train
...
Epoch 1/3
1875/1875 [==============================] - 13s 7ms/step - loss: 1.8391 - accuracy: 0.5231
Epoch 2/3
1875/1875 [==============================] - 18s 10ms/step - loss: 1.0088 - accuracy: 0.6804
Epoch 3/3
1875/1875 [==============================] - 23s 12ms/step - loss: 0.7522 - accuracy: 0.7375
```
### Testing
```
$ python SmartWardrobe.py
Enter command> test
1). hoodie.jpg is an image of a COAT
2). sandal.jpg is an image of SNEAKERS
3). shoe.jpg is an image of ANKLE BOOTS
4). tshirt.jpg is an image of a T-SHIRT/TOP
5). tshirtwhite.jpg is an image of a COAT
```
hoodie.jpg
sandal.jpg
shoe.jpg
tshirt.jpg
tshirtwhite.jpg
## GUI Mode
Built with **PyQt5,** the GUI supports the same functionality as the command line mode and more. In addition to testing images in the img directory, the GUI allows the testing of any other images. The GUI also allows selection of the loss function, the optimizer, and has a built-in terminal and image display.
## Dataset
The [Fashion-MNIST](https://www.kaggle.com/zalando-research/fashionmnist) dataset was used for training.