Discover wolfapple/traffic-sign-recognition Open Source project

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Jupyter Notebook
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MIT License
Created almost 6 years ago
Updated about 4 years ago

wolfapple

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Built and trained a deep neural network to classify traffic signs, using PyTorch. The highlights of this solution would be data preprocessing, trained with heavily augmented data and using Spatial Transformer Network.

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Built and trained a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras. Used optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks.

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CarND-Vehicle-Detection

Detect and track vehicles using color and gradient features and a support vector machine classifier.

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CarND-Traffic-Sign-Classifier-Project

Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting.

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CarND-Semantic-Segmentation

Labeled the pixels of a road in images using a Fully Convolutional Network (FCN).

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wolfapple/traffic-sign-recognition

wolfapple

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