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.
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},
#...]
Useful Resources
Compatible libraries
Note Solid object detection model pre-trained on the COCO 2017 dataset.
Note Real-time and accurate object detection model.
Note Fast and accurate object detection model trained on COCO and Object365 datasets.
Note Multi-task computer vision benchmark.
Note Leaderboard to compare various object detection models across several metrics.
Note An application that contains various object detection models to try from.
Note An application that shows multiple cutting edge techniques for object detection and tracking.
Note An object tracking, segmentation and inpainting application.
Note Very fast object tracking application based on 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