Object Detection in Google Colab with Custom Dataset

This article propose an easy and free solution to train a Tensorflow model for object detection in Google Colab, based on custom datasets. To demonstrate how it works I trained a model to detect my dog in pictures. Object Detection with my dog All the code and dataset used in this article is available in . my Github repo Peculiarities of this proposal are: Only requirement is the dataset created with LabelImg A single Google Colab notebook contains all the steps: it starts from the dataset, executes the model’s training and shows inference It runs in Google Colab’s GPU enabled and Google Drive storage, so it’s based exclusively on free cloud resources Furthermore, important changes have recently been made to Tensorflow’s Object Detection api, that made obsolete other available tutorials. Making dataset The only step not included in the Google Colab notebook is the process to create the dataset. With an appropriate number of photos (my example have 50 photos of dog), I created the annotations. The tool I used is . For the sake of simplicity I identified a single object class, my dog. It’s possible to extend it to obtain models that perform object detection on multiple object classes. LabelImg I renamed the image files in the format (i.e. dog_001.jpg, dog_002.jpg). Then in LabelImg, I defined the bounding box where the object is located, and I saved annotations in Pascal Voc format. objectclass_id.jpg Finally I uploaded annotations files in my Google Drive account, using a single zip file with the following structure: .zip file|-images directory|-image files (filename format: objectclass_id.jpg)|-annotations directory|-xmls directory|-annotations files (filename format: objectclass_id.xml) Check my dataset file in Github to see an example. Dataset example Model training All the next steps are included in the Google Colab notebook. I execute cells in sequence to train the model and run inference: install packages, repositories and environment variables for object detection in Tensorflow, then run a test. Install required packages: download in the filesystem the dataset created. It’s important that the zip file has the structure explained above. Download and extract dataset: this is a cell to avoid error in create_pet_tf_record.py, it has not any effect in training process. Empty png files: from the dataset it creates the TFRecord. In this simplified version, algorithm will train model only for one class. Create TFRecord: download pretrained model from ModelZoo as initial checkpoint for transfer learning. In the example we download the model faster_rcnn_inception_v2_coco, to use another model from ModelZoo change var. Download pretrained model: MODEL MODEL = 'faster_rcnn_inception_v2_coco_2018_01_28'MODEL_FILE = MODEL + '.tar.gz'DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'DEST_DIR = 'pretrained_model' set the fields of the config file, identified by . If you choose a different initial checkpoint model, update accordingly filename var and re.sub functions in the cell. Edit model config file: PATH_TO_BE_CONFIGURED this is the main step, it performs the train of the model with the data and the configurations so far created. It is possible to change the number of steps in train and in validation. Train model: Below the Tensorboard charts resulting from training process: Tensorboard charts Inference export model to run inference. The cell converts last trained model to the format to run inference. Export trained model: browser upload of test image file to run inference in the next step. Upload image for inference: finally it performs inference of the uploaded image, and shows the result below. Run inference: Next goals Thanks a lot for reading my article. In this article we easily trained an object detection model in Google Colab with custom dataset, using Tensorflow framework. If you liked, leave some claps, I will be happy to write more about machine learning. In next articles we will extend the Google Colab notebook to: Include of object detection multiple classes View in a different browser tab during model training Tensorboard Perform to do pixel wise classification instance segmentation