With the recent release of the TensorFlow 2 Object Detection API, it has never been easier to train and deploy state of the art object detection models with TensorFlow leveraging your own custom dataset to detect your own custom objects: foods, pets, mechanical parts, and more.

In this blog and TensorFlow 2 Object Detection Colab Notebook, we walk through how you can train your own custom object detector in minutes, by changing a single line of code for your dataset import.

The TensorFlow Object Detection API 2.0
Train your custom object detector with the TensorFlow2 Object Detection API

In order to train our custom object detector with the TensorFlow 2 Object Detection API we will take the following steps in this tutorial:

Resources included in this tutorial:

Let's get started!

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What is the TensorFlow 2 Object Detection API?

The TensorFlow2 Object Detection API is an extension of the TensorFlow Object Detection API. The TensorFlow2 Object Detection API allows you to train a collection state of the art object detection models under a unified framework, including Google Brain's state of the art model EfficientDet (implemented here).

More generally, object detection models allow you to train your computer to identify objects in a scene with bounding boxes and class labels. There are many ways you can use deep learning techniques to model this problem and the TensorFlow2 Object Detection API allows you deploy a wide variety of different models and strategies to achieve this goal.

Object Detection drawing object detection sketch object detection depiction
A sketch of the object detection task

For a deep dive on the new features in the new library, see our post introducing the TensorFlow 2 Object Detection API.

TensorFlow 2 Object Detection Deep Dive

Learn about the new features included in the new TF2 OD library!

In this tutorial, we train the smallest EfficientDet model (EfficientDet-D0) for detecting our custom objects on GPU resources provided by Google Colab. That said, the TensorFlow 2 Object Detection library has many models available in their model zoo, so you can leverage this tutorial for the following:

Acquiring Labeled Object Detection Data

If you already have a labeled object detection dataset, you can go ahead and skip this section.

Public Datasets

If you are simply trying to get a feel for the new deep learning technologies available in the TensorFlow Object Detection API, you might consider utilizing a public object detection dataset, many of which we make readily available via Roboflow. Consider forking any of these to your account. If you wish to follow along directly with this tutorial, we will be using the public blood cell detection dataset.

Open Source Labeling Solutions

If you have unlabeled images, and want to train a detector to detect your custom objects, we recommend trying your hand at free, open source labeling solutions. Our first choice is Roboflow Annotate, which will flow nicely with this guide.

Labeling objects in Roboflow is a breeze

Alternatively, you may consider other solutions to label your own object detection dataset, such as LabelImg, VoTT, SuperAnnotate, or LabelMe.

Regardless of which tool you use, we recommend exporting your annotations at VOC XML, which you can later convert to any format you need. We find VOC XML is less error prone.

***Using Your Own Data***

To export your own data for this tutorial, sign up for Roboflow and make a public workspace, or make a new public workspace in your existing account. If your data is private, you can upgrade to a paid plan for export to use external training routines like this one or experiment with using Roboflow's internal training solution.

Install TensorFlow 2 Object Detection Dependencies

Once you have a labeled dataset, you are ready to dive into the training procedures.

I recommend opening this Colab Notebook Training TensorFlow2 Object Detection Models, and working through it alongside this blog post. Open and save a copy in Drive so you have your own version of the Colab Notebook.

Google Colab also provides free GPU resources for training, so make sure that is switched on by selecting Runtime --> Change Runtime Type --> GPU.

Then, we install tensorflow_gpu=="2.2.0" as a backbone for our training job.

After that we install the object detection library as a python package.

Keras Bug: There is a bug in exporting TensorFlow2 Object Detection models since the repository is so new. We address this by re-writing one of the Keras utils files. This should disappear in a few days, and we will be updating the notebook accordingly.

Next up, we run the TF2 model builder tests to make sure our environment is up and running. If successful, you should see the following outputs at the end of the cell execution printouts.

[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
Ran 20 tests in 52.705s

OK (skipped=1)
Model tests confirming our TF2 environment setup has been successful 

Prepare TensorFlow 2 Object Detection Training Data

Once our programming environment has been properly installed, we need to acquire a version of our dataset in TFRecord format.

To do so, we recommend using Roboflow for your data transformations. First, sign up for a free account and upload your dataset.

Drag and drop to upload data to Roboflow in any format

After upload you will be prompted to choose options to version your dataset including preprocessing and augmentations.

The augmentation and preprocessing selections for our BCCD dataset

After selecting these options, click Generate and then Download. You will be prompted to choose a data format for your export. Choose Tensorflow TFRecord format.

Note: in addition to creating TFRecords in Roboflow, you can easily inspect the health of your dataset and its annotations as well as preprocess and augment your data to improve model performance.

Selecting TensorFlow TFRecord export format from Roboflow

After export, you will receive a curl link to download your data into our training notebook.

Downloading our training TFRecords in the TF2 Object Detection Colab Notebook

Lastly, we map our training data files to variables for use in our computer vision training pipeline configuration.

Write Custom TensorFlow 2 Object Detection Training Configuration

Next, we write a specialized training configuration file based on our choice of an object detection model to instruct the training procedure we plan to run later in the notebook.

By changing the chosen_model variable, you can select among available models for training. We have coded in the first few models in the EfficientDet model series for your exploration. If you want to use even bigger EfficientDet models, you will likely need to increase your compute resources beyond Google Colab!

EfficientDet model family is among state of the art for object detection

You may also consider adding any model you desire in the TensorFlow 2 Object Detection model zoo.

Each model has a model_name, a base_pipeline_file, a pretrained_checkpoint, and a batch_size. The base_pipeline_file is a shell of a training configuration specific to each model type, provided by the authors of the TF2 OD repository. The pretrained_checkpoint is the location of a pretrained weights file saved from when the object detection model was pretrained on the COCO dataset. We will start from these weights, and then fine tune into our particular custom dataset task. By using pretraining, our model does not need to start from square one in identifying what features might be useful for object detection.

With all of those inputs defined, we edit the base_pipeline_file to point to our custom data, the pretrained_checkpoint, and we also specify some training parameters. To train for longer, increase the num_steps and to train faster, try increasing the batch_size to a level that your GPU can handle. Remember to decrease the number of steps with the same factor that you increase batch size to keep the training length the same.

Train Custom TensorFlow 2 Object Detector

Now we are ready to train!

We kick of training with the following command:

!python /content/models/research/object_detection/model_main_tf2.py \
    --pipeline_config_path={pipeline_file} \
    --model_dir={model_dir} \
    --alsologtostderr \
    --num_train_steps={num_steps} \
    --sample_1_of_n_eval_examples=1 \

Our training command references our pipeline_file and the model_dir where we would like the model to be saved during training.

In Colab, even with free GPU, you should expect training to be a lengthy multi-hour process. Note that Colab will stop your kernel's session after a period of inactivity (around 45 minutes), so you may need to keep interacting in your browser tab.

Training Evaluation

At this time of writing, training time evaluation metrics are still under construction for EfficientDet. The code is there so you can try it! If you find out how to execute this part of the notebook, please drop us a line! This does not affect other architectures, and TensorBoard eval is still valid.

TensorBoard Output

To examine our training success, we output the TensorBoard showing how our models loss function has decreased over the training procedure. The lower the loss, the better!

TensorBoard output to visualize our training procedure

Export Trained TensorFlow 2 Object Detector Weights

Next, we use the exporter_main_v2.py to export our model from TF2 OD library checkpoints to a .pb frozen graph file. The .pb file will be much easier for us to deploy to applications and move to new settings. Stay tuned for more to come on this front.

Use Trained TensorFlow 2 Object Detection For Inference on Test Images

Now that we have a trained TensorFlow2 object detector, we can use our model's saved weights to make test inference on images that the model has never seen.

TFRecord does not allow you to access the underlying images, so we recommend making another export of your dataset in COCO JSON format to access the underlying test images.

We rebuild our custom object detector from the saved checkpoints. Generally, you want to choose the last checkpoint but you may also want to choose the checkpoint where your object detector did the best on your validation set.

We draw from our test images randomly and send the image through the network for prediction.

Test inference on an image that our model has never seen

Our model does a pretty good job of detecting different types of cells in the blood stream!

With the right dataset, you can apply this technology to teach the model to recognize any object in the world.


Congratulations! Now you know how to train custom object detection models using the TensorFlow 2 Object Detection API toolkit.

The TensorFlow 2 Objection Detection API allows you immense flexibility to switch between state of the art computer vision techniques for the detection of your custom objects.

We hope you enjoyed! As always, happy detecting.