Tasks

Object Detection

Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.

Inputs
Object Detection Model
Output

About Object Detection

Use Cases

Autonomous Driving

Object Detection is widely used in computer vision for autonomous driving. Self-driving cars use Object Detection models to detect pedestrians, bicycles, traffic lights and road signs to decide which step to take.

Object Tracking in Matches

Object Detection models are widely used in sports where the ball or a player is tracked for monitoring and refereeing during matches.

Image Search

Object Detection models are widely used in image search. Smartphones use Object Detection models to detect entities (such as specific places or objects) and allow the user to search for the entity on the Internet.

Object Counting

Object Detection models are used to count instances of objects in a given image, this can include counting the objects in warehouses or stores, or counting the number of visitors in a store. They are also used to manage crowds at events to prevent disasters.

Inference

You can infer with Object Detection models through the object-detection pipeline. When calling the pipeline you just need to specify a path or http link to an image.

model = pipeline("object-detection")

model("path_to_cat_image")

# [{'label': 'blanket',
#  'mask': mask_string,
#  'score': 0.917},
#...]

Compatible libraries

Transformers
Object Detection demo
Object Detection
Examples
Examples
Drag image file here or click to browse from your device
This model can be loaded on the Inference API on-demand.
Models for Object Detection Browse Models (28)

Note Solid object detection model trained on the benchmark dataset COCO 2017.

Note Strong object detection model trained on ImageNet-21k dataset.

Metrics for Object Detection
Average Precision
The Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It is calculated for each class separately
Mean Average Precision
The Mean Average Precision (mAP) metric is the overall average of the AP values
APα
The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75