# Object detection

<img src="../images/detection.png" class="attempt-right">

Detect multiple objects within an image, with bounding boxes. Recognize 80
different classes of objects.

## Get started

If you are new to TensorFlow Lite and are working with Android or iOS, we
recommend exploring the following example applications that can help you get
started.

<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android">Android
example</a>
<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/ios">iOS
example</a>

If you are using a platform other than Android or iOS, or you are already
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
download our starter object detection model and the accompanying labels.

<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
starter model and labels</a>

For more information about the starter model, see
<a href="#starter_model">Starter model</a>.

## What is object detection?

Given an image or a video stream, an object detection model can identify which
of a known set of objects might be present and provide information about their
positions within the image.

For example, this screenshot of our <a href="#get_started">example
application</a> shows how two objects have been recognized and their positions
annotated:

<img src="images/android_apple_banana.png" alt="Screenshot of Android example" width="30%">

An object detection model is trained to detect the presence and location of
multiple classes of objects. For example, a model might be trained with images
that contain various pieces of fruit, along with a _label_ that specifies the
class of fruit they represent (e.g. an apple, a banana, or a strawberry), and
data specifying where each object appears in the image.

When we subsequently provide an image to the model, it will output a list of the
objects it detects, the location of a bounding box that contains each object,
and a score that indicates the confidence that detection was correct.

### Model output

Imagine a model has been trained to detect apples, bananas, and strawberries.
When we pass it an image, it will output a set number of detection results - in
this example, 5.

<table style="width: 60%;">
  <thead>
    <tr>
      <th>Class</th>
      <th>Score</th>
      <th>Location</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Apple</td>
      <td>0.92</td>
      <td>[18, 21, 57, 63]</td>
    </tr>
    <tr>
      <td>Banana</td>
      <td>0.88</td>
      <td>[100, 30, 180, 150]</td>
    </tr>
    <tr>
      <td>Strawberry</td>
      <td>0.87</td>
      <td>[7, 82, 89, 163] </td>
    </tr>
    <tr>
      <td>Banana</td>
      <td>0.23</td>
      <td>[42, 66, 57, 83]</td>
    </tr>
    <tr>
      <td>Apple</td>
      <td>0.11</td>
      <td>[6, 42, 31, 58]</td>
    </tr>
  </tbody>
</table>

### Confidence score

To interpret these results, we can look at the score and the location for each
detected object. The score is a number between 0 and 1 that indicates confidence
that the object was genuinely detected. The closer the number is to 1, the more
confident the model is.

Depending on your application, you can decide a cut-off threshold below which
you will discard detection results. For our example, we might decide a sensible
cut-off is a score of 0.5 (meaning a 50% probability that the detection is
valid). In that case, we would ignore the last two objects in the array, because
those confidence scores are below 0.5:

<table style="width: 60%;">
  <thead>
    <tr>
      <th>Class</th>
      <th>Score</th>
      <th>Location</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Apple</td>
      <td>0.92</td>
      <td>[18, 21, 57, 63]</td>
    </tr>
    <tr>
      <td>Banana</td>
      <td>0.88</td>
      <td>[100, 30, 180, 150]</td>
    </tr>
    <tr>
      <td>Strawberry</td>
      <td>0.87</td>
      <td>[7, 82, 89, 163] </td>
    </tr>
    <tr>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">Banana</td>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">0.23</td>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">[42, 66, 57, 83]</td>
    </tr>
    <tr>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">Apple</td>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">0.11</td>
      <td style="background-color: #e9cecc; text-decoration-line: line-through;">[6, 42, 31, 58]</td>
    </tr>
  </tbody>
</table>

The cut-off you use should be based on whether you are more comfortable with
false positives (objects that are wrongly identified, or areas of the image that
are erroneously identified as objects when they are not), or false negatives
(genuine objects that are missed because their confidence was low).

For example, in the following image, a pear (which is not an object that the
model was trained to detect) was misidentified as a "person". This is an example
of a false positive that could be ignored by selecting an appropriate cut-off.
In this case, a cut-off of 0.6 (or 60%) would comfortably exclude the false
positive.

<img src="images/false_positive.png" alt="Screenshot of Android example showing a false positive" width="30%">

### Location

For each detected object, the model will return an array of four numbers
representing a bounding rectangle that surrounds its position. For the starter
model we provide, the numbers are ordered as follows:

<table style="width: 50%; margin: 0 auto;">
  <tbody>
    <tr style="border-top: none;">
      <td>[</td>
      <td>top,</td>
      <td>left,</td>
      <td>bottom,</td>
      <td>right</td>
      <td>]</td>
    </tr>
  </tbody>
</table>

The top value represents the distance of the rectangle’s top edge from the top
of the image, in pixels. The left value represents the left edge’s distance from
the left of the input image. The other values represent the bottom and right
edges in a similar manner.

Note: Object detection models accept input images of a specific size. This is likely to be different from the size of the raw image captured by your device’s camera, and you will have to write code to crop and scale your raw image to fit the model’s input size (there are examples of this in our <a href="#get_started">example applications</a>).<br /><br />The pixel values output by the model refer to the position in the cropped and scaled image, so you must scale them to fit the raw image in order to interpret them correctly.

## Starter model

We recommend starting with this pre-trained quantized COCO SSD MobileNet v1
model.

<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
starter model and labels</a>

### Uses and limitations

The object detection model we provide can identify and locate up to 10 objects
in an image. It is trained to recognize 80 classes of object. For a full list of
classes, see the labels file in the
<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
zip</a>.

If you want to train a model to recognize new classes, see
<a href="#customize_model">Customize model</a>.

For the following use cases, you should use a different type of model:

<ul>
  <li>Predicting which single label the image most likely represents (see <a href="../image_classification/overview.md">image classification</a>)</li>
  <li>Predicting the composition of an image, for example subject versus background (see <a href="../segmentation/overview.md">segmentation</a>)</li>
</ul>

### Input

The model takes an image as input. The expected image is 300x300 pixels, with
three channels (red, blue, and green) per pixel. This should be fed to the model
as a flattened buffer of 270,000 byte values (300x300x3). Since the model is
<a href="../../performance/post_training_quantization.md">quantized</a>, each
value should be a single byte representing a value between 0 and 255.

### Output

The model outputs four arrays, mapped to the indices 0-4. Arrays 0, 1, and 2
describe 10 detected objects, with one element in each array corresponding to
each object. There will always be 10 objects detected.

<table>
  <thead>
    <tr>
      <th>Index</th>
      <th>Name</th>
      <th>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>0</td>
      <td>Locations</td>
      <td>Multidimensional array of [10][4] floating point values between 0 and 1, the inner arrays representing bounding boxes in the form [top, left, bottom, right]</td>
    </tr>
    <tr>
      <td>1</td>
      <td>Classes</td>
      <td>Array of 10 integers (output as floating point values) each indicating the index of a class label from the labels file</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Scores</td>
      <td>Array of 10 floating point values between 0 and 1 representing probability that a class was detected</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Number and detections</td>
      <td>Array of length 1 containing a floating point value expressing the total number of detection results</td>
    </tr>
  </tbody>
</table>

## Customize model

The pre-trained models we provide are trained to detect 80 classes of object.
For a full list of classes, see the labels file in the
<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
zip</a>.

You can use a technique known as transfer learning to re-train a model to
recognize classes not in the original set. For example, you could re-train the
model to detect multiple types of vegetable, despite there only being one
vegetable in the original training data. To do this, you will need a set of
training images for each of the new labels you wish to train.

Learn how to perform transfer learning in
<a href="https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193">Training
and serving a real-time mobile object detector in 30 minutes</a>.
