https://github.com/lovesaroha/rock-paper-scissor-classification-training

Training a keras model to recognize real images of rock, paper, scissors (hand) in order to classify an incoming image.
https://github.com/lovesaroha/rock-paper-scissor-classification-training

keras machine-learning python rock-paper-scissor transfer-learning

Last synced: 18 days ago
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Training a keras model to recognize real images of rock, paper, scissors (hand) in order to classify an incoming image.

• Host: GitHub
• URL: https://github.com/lovesaroha/rock-paper-scissor-classification-training
• Owner: lovesaroha
• License: gpl-3.0
• Created: 2021-09-26T13:14:59.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-09-26T18:07:34.000Z (over 3 years ago)
• Last Synced: 2024-11-13T10:48:25.906Z (3 months ago)
• Topics: keras, machine-learning, python, rock-paper-scissor, transfer-learning
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Rock-Paper-Scissor-Classification-Training

Training a keras model to recognize real images of rock, paper, scissors (hand) in order to classify an incoming image. 


Download training images from [laurencemoroney-blog.appspot.com/rps.zip](https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip).
 

Download validation images from [laurencemoroney-blog.appspot.com/rps-test-set.zip](https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip). 


Download inception model weights for transfer learning from [mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5](https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5)

## Requirements

- Python 3 or higher.

## Packages

### Numpy

```bash

py -m pip install numpy

```

### Tensorflow

```bash

py -m pip install tensorflow

```

## Start Training

```bash

py convolutional_neural_network.py

```

```bash

py transfer_learning.py

```