georgesung / ssd_vehicle_detection

Vehicle Detection with SSD in TensorFlow

This is adapted from [https://github.com/georgesung/ssd_tensorflow_traffic_sign_detection]

The code was slightly modified, and the model was trained using the Udacity vehicle detection dataset: [https://github.com/udacity/self-driving-car/tree/master/annotations]

Here is a demo video of video detection with this implementation: [https://www.youtube.com/watch?v=Ha1QbnuDYJU]

Dependencies

• Python 3.5+
• TensorFlow v0.12.0
• Pickle
• OpenCV-Python
• Matplotlib (optional)

How to run

Note this was copy and pasted from the traffic sign detection project, the process is included here for reference. BUT, the link to the pre-trained model is updated :)

Clone this repository somewhere, let's refer to it as $ROOT

To run predictions using the pre-trained model:

• Download the pre-trained model to $ROOT
• cd $ROOT
• python inference.py -m demo
  • This will take the images from sample_images, annotate them, and display them on screen
• To run predictions on your own images and/or videos, use the -i flag in inference.py (see the code for more details)
  • Note the model severly overfits at this time

Training the model from scratch: TODO: Modify the steps below to work with vehicle dataset

• Download the LISA Traffic Sign Dataset, and store it in a directory $LISA_DATA
• cd $LISA_DATA
• Follow instructions in the LISA Traffic Sign Dataset to create 'mergedAnnotations.csv' such that only stop signs and pedestrian crossing signs are shown
• cp $ROOT/data_gathering/create_pickle.py $LISA_DATA
• python create_pickle.py
• cd $ROOT
• ln -s $LISA_DATA/resized_images_* .
• ln -s $LISA_DATA/data_raw_*.p .
• python data_prep.py
  • This performs box matching between ground-truth boxes and default boxes, and packages the data into a format used later in the pipeline
• python train.py
  • This trains the SSD model
• python inference.py -m demo