Deploying Object Detection Models to Production with TensorFlow Serving

Object detection is a powerful computer vision technique with applications ranging from facial recognition to autonomous driving. Using deep learning models like SSD, YOLO, and Faster R-CNN, we can achieve impressive accuracy in locating and classifying objects in images and video.

However, building an accurate detection model is only half the challenge. To provide real value, the model needs to be integrated into a production system that can apply it to new data efficiently and reliably. This is where TensorFlow Serving comes in.

TensorFlow Serving is a high-performance system for serving machine learning models, designed for production environments. It provides a flexible, uniform architecture for deploying models from many frameworks, while optimizing for inference speed and resource efficiency.

In this guide, we‘ll walk through the process of deploying a state-of-the-art object detection model using TensorFlow Serving. We‘ll cover exporting the model from the TensorFlow Object Detection API, building a serving environment with Docker, creating an inference client, and strategies for performance optimization and scaling.

By the end, you‘ll have a blueprint for bringing your own object detection models to production, with the robustness and performance needed for real-world applications. Let‘s dive in!

The TensorFlow Object Detection API

The TensorFlow Object Detection API is an open-source framework that makes it easy to construct, train, and deploy object detection models. The API supports several state-of-the-art architectures, including:

• Single Shot Multibox Detector (SSD) with MobileNets
• SSD with Inception V2
• Region-Based Fully Convolutional Networks (R-FCN) with ResNets
• Faster R-CNN with ResNets
• Mask R-CNN

These architectures strike different balances between speed and accuracy. For example, SSD models are generally faster but less precise than Faster R-CNN. The choice of architecture ultimately depends on the specific requirements of your application.

The Object Detection API also provides a complete workflow for training models on popular datasets like COCO, KITTI, and Open Images. It includes scripts for converting datasets to the TFRecord format, configuring hyperparameters, and running training and evaluation.

Once you‘ve trained a model that meets your accuracy targets, the next step is to export it in a format that TensorFlow Serving can load and serve. We‘ll cover this process in the next section.

Exporting a Model for Serving

To serve a model with TensorFlow Serving, you first need to export it as a SavedModel. This is a language-neutral, recoverable, hermetic serialization format that includes a TensorFlow program and its weights.

The Object Detection API provides a script called export_inference_graph.py for exporting trained models. However, this script exports models in a frozen graph format that isn‘t compatible with TensorFlow Serving. To export a SavedModel, you‘ll need to make a few modifications:

1. Use tf.saved_model.simple_save instead of write_graph_and_checkpoint to save the model.
2. Specify the inputs and outputs in the signature_def_map to define the model‘s interface for inference.
3. Provide a serving_input_receiver_fn that parses the incoming request into the input tensors expected by the model.

Here‘s an example of what the modified export function might look like:

def export_model(model_dir, export_dir):
    with tf.Graph().as_default() as graph:
        ...
        inputs = tf.placeholder(tf.uint8, [None, None, None, 3], name=‘input_tensor‘)
        detections = detect_fn(inputs)

        saver = tf.train.Saver()
        with tf.Session() as sess:
            saver.restore(sess, last_ckpt)

            builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
            serving_input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
                ‘inputs‘: tf.placeholder(shape=[None, None, None, 3], dtype=tf.uint8)
            })

            builder.add_meta_graph_and_variables(sess, 
                                                 [tf.saved_model.tag_constants.SERVING],
                                                 signature_def_map={
                ‘serving_default‘: tf.estimator.export.PredictOutput(detections)
            },
                                                 assets_collection=None,
                                             )
            builder.save()

This exports the model with a serving signature that accepts a batch of images as input, and returns the detections as output. You can customize the signature to fit your specific input and output requirements.

Running the modified export script will produce a SavedModel in a timestamped directory under the specified export path. This directory contains all the files needed to load and serve the model with TensorFlow Serving.

Building a TensorFlow Serving Environment

With the exported model in hand, the next step is to set up TensorFlow Serving. The easiest way to get started is using the official Docker image, which bundles TensorFlow Serving with its dependencies in a portable, pre-built package.

To run TensorFlow Serving with Docker, first pull the latest image:

docker pull tensorflow/serving

Then, start a container with the exported model mounted in a local directory:

docker run -p 8501:8501 \
  --mount type=bind,source=/path/to/export,target=/models/mymodel \
  -e MODEL_NAME=mymodel -t tensorflow/serving

This command starts TensorFlow Serving and loads the model at /models/mymodel. It also maps port 8501 of the container to the host machine, so you can send inference requests.

With the server running, you can now connect to it from a client application to get object detection results. We‘ll look at building a client in the next section.

Connecting a Client for Inference

To send images to the model server and get detection results, you need to create a client that implements the TensorFlow Serving REST or gRPC API. For this example, we‘ll use the gRPC API and create a simple Python client.

First, install the tensorflow-serving-api package:

pip install tensorflow-serving-api

This provides the Python bindings for the TensorFlow Serving API. Next, create a client script that loads an image, sends it to the server, and processes the detection results:

from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
import grpc
import cv2
import numpy as np

channel = grpc.insecure_channel(‘localhost:8501‘)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

img = cv2.imread(‘image.jpg‘)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.expand_dims(img, axis=0)

request = predict_pb2.PredictRequest()
request.model_spec.name = ‘mymodel‘
request.model_spec.signature_name = ‘serving_default‘
request.inputs[‘inputs‘].CopyFrom(tf.make_tensor_proto(img, shape=img.shape))

result = stub.Predict(request)

boxes = result.outputs[‘detection_boxes‘].float_val
scores = result.outputs[‘detection_scores‘].float_val
classes = result.outputs[‘detection_classes‘].float_val

This script loads an image using OpenCV, converts it to RGB, and adds a batch dimension. It then creates a PredictRequest with the model name and signature, and copies the image data into the request inputs.

Sending this request to the server returns a PredictResponse containing the detection boxes, scores, and classes. You can use these to filter and visualize the results.

Optimizing Performance and Scalability

With the basic serving workflow in place, you can now look at optimizing the system for your production requirements. TensorFlow Serving provides several features for improving inference performance and scaling to handle large workloads:

• Batching: You can configure the model server to batch individual requests into larger inference runs. This amortizes the overhead of loading the model and can significantly improve throughput. To enable batching, set the --enable_batching flag when starting the server, and configure the batching parameters as needed.

• GPU Acceleration: TensorFlow Serving can run on GPUs for faster inference, especially with complex models. To use a GPU, install the GPU version of TensorFlow Serving and set the --gpu flag when starting the server. You‘ll also need to configure the model and client to use the appropriate GPU device.

• Model Ensembling: Running multiple versions of a model and combining their outputs can improve accuracy and robustness. TensorFlow Serving supports model versioning and A/B testing out of the box. You can deploy multiple versions of a model, dynamically route requests to specific versions, and aggregate the results on the client side.

• Horizontal Scaling: To handle higher request volumes, you can scale out TensorFlow Serving by running multiple instances behind a load balancer. Tools like Kubernetes make it easy to deploy and manage a cluster of serving instances, and automatically scale the number of replicas based on load.

By applying these techniques, you can build an efficient, scalable infrastructure for serving object detection models (and other types of models) in production.

Real-World Examples and Best Practices

To get a sense of what‘s possible with TensorFlow Serving, let‘s look at a few real-world deployments and their performance characteristics:

• Facebook uses TensorFlow Serving to power features like automatic alt text and photo search. They‘ve found that serving models in production with TensorFlow Serving is 2-4x faster than using a Python-based Flask server, with latency in the 10s of milliseconds.

• Spotify uses TensorFlow Serving to personalize music recommendations in real-time. By combining efficient serving with careful model design, they‘ve achieved a 21% increase in overall accuracy and a 41% increase in per-user accuracy.

• Ebay uses TensorFlow Serving to apply computer vision models to listing photos in search and recommendation. They‘ve found that using GPUs with TensorFlow Serving achieves a 7x speedup over CPUs, and enables them to scale to over 1 billion listing images.

These examples show what‘s possible with a well-optimized TensorFlow Serving deployment. To achieve similar results, here are a few best practices to keep in mind:

• Profile and optimize your models before deploying to production. Make sure you‘re using an appropriate architecture for your latency and throughput requirements.
• Take advantage of batching and GPU acceleration to get the most efficient use of resources. Experiment with different batch sizes and GPU configurations to find the optimal balance.
• Use model versioning and A/B testing to safely roll out new models and compare their performance to previous versions. This helps catch regressions and enables continuous improvement.
• Monitor your serving infrastructure for latency, throughput, and error rates. Use this data to identify bottlenecks and optimization opportunities.
• Have a fallback plan for handling server failures or overload. Implement load shedding, circuit breaking, and other resilience patterns to maintain a good user experience under adverse conditions.

Conclusion

Deploying object detection models to production is a complex challenge, but TensorFlow Serving provides a set of powerful tools to make it easier. By exporting models in the right format, running the server with Docker, and connecting a client to send inference requests, you can build a complete serving solution for TensorFlow models.

You can then optimize and scale this system to achieve the performance required for real-world use cases, from large-scale photo tagging to real-time video analysis. With careful design and proven best practices, TensorFlow Serving can be a key component of production machine learning infrastructure.

Of course, TensorFlow Serving is just one part of the MLOps ecosystem. Putting a model into production also requires robust pipelines for data ingestion, training, evaluation, and monitoring. But by providing a standard, high-performance inference layer, TensorFlow Serving helps simplify the path from experimentation to real-world impact with machine learning.