experiencor/semantic-lane-detection

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Created almost 7 years ago
Updated almost 7 years ago

experiencor/semantic-lane-detection

experiencor

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A robust lane detection system based on fully convolutional network for segmenting the road and the lane.

keras-yolo2

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Jupyter Notebook

keras-yolo3

Training and Detecting Objects with YOLO3

self-driving-toy-car

A self driving toy car using end-to-end learning

Jupyter Notebook

image-to-3d-bbox

Build a CNN network to predict 3D bounding box of car from 2D image.

Jupyter Notebook

deep-viz-keras

Implementations of some popular Saliency Maps in Keras

Jupyter Notebook

kangaroo

A dataset of 183 images and annotations for kangaroo detection.

speed-prediction

Use 3D convolutional network to predict the speed of a car via dashcam stream. The final mean squared error on validation set is 3.1 (mile/hour)^2.

Jupyter Notebook

online-machine-learning

Implementation of an online learning algorithm to do classification under concept drift

Jupyter Notebook

basic-rnn-lstm

Implementations of vanilla RNN and LSTM from scratch.

Jupyter Notebook

basic-machine-learning

Implementations of basic machine learning algorithms

Jupyter Notebook

rasa-play

deep-fake