https://github.com/thevickypedia/csc790_deeplearning
Convolutional Neural Network using Jupyter Notebooks
https://github.com/thevickypedia/csc790_deeplearning
cnn convolutional-neural-networks deep-learning image-classification neural-network traffic-sign-classifier traffic-sign-recognition
Last synced: 2 months ago
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Convolutional Neural Network using Jupyter Notebooks
- Host: GitHub
- URL: https://github.com/thevickypedia/csc790_deeplearning
- Owner: thevickypedia
- Created: 2019-03-31T00:18:28.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-01-08T22:29:58.000Z (over 2 years ago)
- Last Synced: 2025-02-12T22:23:08.174Z (4 months ago)
- Topics: cnn, convolutional-neural-networks, deep-learning, image-classification, neural-network, traffic-sign-classifier, traffic-sign-recognition
- Language: Jupyter Notebook
- Homepage:
- Size: 16.2 MB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# CSC790_DeepLearning
## Traffic Signs Classifier
This project was meant for learning image classification using Convolutional Neural Networks.
Additionally, this project compares results between [Adam](https://keras.io/api/optimizers/adam/) and
[SGD](https://keras.io/api/optimizers/sgd/) optimizers.
### Notebooks
- [Traffic Signs Classifier using Adam optimizer](CNN_TSR_Final-Adam.ipynb)
- [Traffic Signs Classifier using SGD optimizer](CNN_TSR_Final-Gradient_Descent.ipynb)
> - Both the notebooks use ipython to run on Jupyter
> - This project is set to create notebook specific `venv` installing requirements during startup
> - Dataset is automatically downloaded from
> [github](https://github.com/thevickypedia/open-source/tree/main/traffic-signs-data) and unzipped in real-time
### Concepts Used:
* Batch Normalization
* Data Augmentation
* Drop Out
### Optimizers
* Adam
* SGD (Stochastic Gradient Descent)
### Project Report
- [Final Report](materials/report/CSC790_TrafficSignClassifier_Final_Report.docx)
- [Presentation](materials/report/CSC790_TrafficSignClassifier_Slides.pptx)
### Results
- [Screenshots](materials/results)
### Notes and credits
- [Notes](materials/markdown/notes.md)
- [Credits](materials/markdown/credits.md)