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https://github.com/adarshkoppmanjunath/produceitemrecognitionusingcnn
Produce Item Recognition- Dataset is created from scratch, has train, validation, and test set. Train is with 800 images per class, test and validation are with 320 images per class. Totally dataset has 24 classes. I have used CNN and VGG16 for the model training.
https://github.com/adarshkoppmanjunath/produceitemrecognitionusingcnn
cnn-keras conda kivy machine-learning numpy opencv pandas python
Last synced: about 1 month ago
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Produce Item Recognition- Dataset is created from scratch, has train, validation, and test set. Train is with 800 images per class, test and validation are with 320 images per class. Totally dataset has 24 classes. I have used CNN and VGG16 for the model training.
- Host: GitHub
- URL: https://github.com/adarshkoppmanjunath/produceitemrecognitionusingcnn
- Owner: AdarshKoppManjunath
- Created: 2019-12-18T07:46:54.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-30T08:10:32.000Z (over 4 years ago)
- Last Synced: 2023-03-04T13:07:58.567Z (almost 2 years ago)
- Topics: cnn-keras, conda, kivy, machine-learning, numpy, opencv, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 2.5 GB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0