Building custom traffic dataset

Even though pre-trained checkpoints are really useful, most of the time you will want to train an object detector using your own dataset. For this, you need a source of images and their corresponding bounding box coordinates and labels, in some format that Luminoth can understand. In this case, we are interested in street traffic related objects, so we will need to source images relevant to our niche.

How Luminoth handles datasets

Luminoth reads datasets natively only in TensorFlow’s TFRecords format. This is a binary format that will let Luminoth consume the data very efficiently.

In order to use a custom dataset, you must first transform whatever format your data is in, to TFRecords files (one for each split — train, val, test). Fortunately, Luminoth provides several CLI tools (see Adapting a dataset) for transforming popular dataset format (such as Pascal VOC, ImageNet, COCO, CSV, etc.) into TFRecords. In what follows, we will leverage this.

Building a traffic dataset using OpenImages

OpenImages V4 is the largest existing dataset with object location annotations. It contains 15.4M bounding-boxes for 600 categories on 1.9M images, making it a very good choice for getting example images of a variety of (not niche-domain) classes (persons, cars, dolphin, blender, etc).

Preparing the data

We should start by downloading the annotation files (this and this, for train) and the class description file. Note that the files with the annotations themselves are pretty large, totalling over 1.5 GB (and this CSV files only, without downloading a single image!).

After we get the class-descriptions-boxable.csv file, we can go over all the classes available in the OpenImages dataset and see which ones are related to traffic. The following were hand-picked after examining the full file:

/m/015qff,Traffic light

Using the Luminoth dataset reader

Luminoth includes a dataset reader that can take OpenImages format. As the dataset is so large, this will never download every single image, but fetch only those we want to use and store them directly in the TFRecords file.

Note that the dataset reader expects a particular directory layout so it knows where the files are located. In this case, files corresponding to the examples must be in a folder named like their split (train, test, …). So, you should have the following:

├── class-descriptions-boxable.csv
└── train
    ├── train-annotations-bbox.csv
    └── train-annotations-human-imagelabels-boxable.csv

Next, run the following command:

lumi dataset transform \
      --type openimages \
      --data-dir . \
      --output-dir ./out \
      --split train  \
      --class-examples 100 \

This will generate TFRecord file for the train split. You should get something like this in your terminal after the command finishes:

INFO:tensorflow:Saved 360 records to "./out/train.tfrecords"
INFO:tensorflow:Composition per class (train):
INFO:tensorflow:        Person (/m/01g317): 380
INFO:tensorflow:        Car (/m/0k4j): 255
INFO:tensorflow:        Bicycle (/m/0199g): 126
INFO:tensorflow:        Bus (/m/01bjv): 106
INFO:tensorflow:        Traffic light (/m/015qff): 105
INFO:tensorflow:        Truck (/m/07r04): 101
INFO:tensorflow:        Van (/m/0h2r6): 100
INFO:tensorflow:        Motorcycle (/m/04_sv): 100

Apart from the TFRecord file, you will also get a classes.json file that lists the names of the classes in your dataset.

Note that:

  • As we are using --only-classes, so we filter to only use the classes we care about.
  • We are using --max-per-class of 100. This setting will make it stop when every class has at least 100 examples. However, some classes may end up with many more; for example here it needed to get 380 instances of persons to get 100 motorcycles, considering the first 360 images.
  • We could also have used --limit-examples option so we know the number of records in our final dataset beforehand.

Of course, this dataset is way too small for any meaningful training to go on, but we are just showcasing. In real life, you would use a much larger value for --max-per-class (ie. 15000) or --limit-examples.

Next: Training the model