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A repository for code for the rob535 perception project
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csvs added csv write folder Dec 11, 2018
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models init the models folder to the repo Nov 30, 2018
task2 removed old code, streamlined Dec 11, 2018
.gitignore added csvs to gitignore Dec 14, 2018
LICENSE squeeze and excitation nets implemented Nov 19, 2018 Update Dec 20, 2018 last csv got 0.12 accuracy.. needs fixing... Dec 21, 2018
classes.csv cleaned up readme and files, added demo code Nov 19, 2018
requirements.txt proper requirement.txt Dec 11, 2018 last csv got 0.12 accuracy.. needs fixing... Dec 21, 2018

ROB535 Team 9 Perception Repo

This repository contains code to classify images for the Perception portion of ROB535.

Currently we finetune pretrained models from the package cnn_finetune to our dataset, including data augmentation from other datasets.

We also have a number of tasks which can be run.

Getting additional data running

Note: extra data is not stored in folders like our data. It has one folder for all images and one folder for all annotations. Also, annotations are stored in VOC files, and do not have the same 23 class labels as our .bin files. For these reason we load it separately in

  1. in /home/ubuntu/more_train/ download either 10k imgs or all 200k imgs (and annotations) from
  2. extract them: $ tar zxvf repro_10k_images.tgz $ tar zxvf repro_10k_annotations.tgz
  3. move annotations and images folders back to /home/ubuntu/more_train/
  4. run as usual! May want to experiment with 3 classes vs. all, but seems to help! Appears to be marginally slower (maybe 20% slower per iteration)

Environment setup

To make a conda environment for our code, run conda env create -f env535.yml Then, source activate env535.yml and run python3 with appropriate args

Old versions included:

It contains the code for Squeeze and Excitation Networks:


It contains demo code provided by the instructors:

  • classes.csv

It contains our custom code.

  1. Calculating the mean and standard deviation of the dataset:
  • mean_std.txt
  1. Running our CarNet model

CarNet is a slightly modified version of the squeeze and excitation ResNet. It contains 4 residual blocks instead of the neighboring 3 and 5. CarNet is trained to predict which of the 23 object classes an image contains.

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