https://github.com/ksatriow/iclassify

Image Classification to recognize the shape of the hand that forms scissors, rock, or paper.
https://github.com/ksatriow/iclassify

cnn image-augmentation image-classification machine-learning tensorflow

Last synced: 28 days ago
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Image Classification to recognize the shape of the hand that forms scissors, rock, or paper.

• Host: GitHub
• URL: https://github.com/ksatriow/iclassify
• Owner: ksatriow
• License: apache-2.0
• Created: 2021-05-07T06:29:33.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-05-07T07:28:39.000Z (over 4 years ago)
• Last Synced: 2024-12-29T07:43:57.813Z (about 1 year ago)
• Topics: cnn, image-augmentation, image-classification, machine-learning, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 459 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# iClassify

iClassify -> Image Classification to recognize the shape of the hand that forms scissors, rock, or paper.

## Convolutional Neural Network (CNN)

To be updated soon.

### Spread Some :heart:

[![GitHub followers](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ksatriow)  [![Linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kukuh-satrio-wibowo/) 

### Implementation

* [The dataset is divided into train sets and validation sets.]

* [The size of the validation set must be 40% of the total dataset (the training data has 1314 samples, and the validation data is 874 samples).]

* [Implements image augmentation.]

* [Using image data generator.]

* [Model training < 30 minutes.]

* [Using Google Colaboratory.]

* [The accuracy is 98%.]

* [Can predict images]

## Steps

1. Install the modules required based on the type of implementation.

2. Download the dataset you want to train and predict your system with. (https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip)

3. Train your data using Google Colab (https://colab.research.google.com/)

## Certificate

![](/assets/certificate_basic_ml.jpg)

## Review Result

![](/assets/review.png)

# Contribution

I highly encourage the community to step forward and improve this code further. You can fix any reported bug, propose or implement new features, write tests, etc.

Here is a quick list of things to remember -

* Check the open issues before creating a new one,

* Help me in reducing the number of open issues by fixing any existing bugs,

* Check the roadmap to see if you can help in implementing any new feature,

* You can contribute by writing unit and integration tests for this library,

* If you have any new idea that aligns with the goal of this library, feel free to raise a feature request and discuss it.

# About The Author

### Kukuh Satrio Wibowo

Skilled Android, DevOps and IoT Engineer with 3+ years of hands-on experience supporting, automating, and optimizing mission critical deployments in AWS, leveraging configuration management, CI/CD, and DevOps processes. 



# License

```

Copyright 2021 ksatriow

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.