What libraries can I use for Object Detection?

The transformersand transformers.js libraries are compatible with Object Detection.

What models can I use for Object Detection?

The facebook/detr-resnet-50, jameslahm/yolov10x, and PekingU/rtdetr_r18vd_coco_o365 models can be used for Object Detection.

What datasets can I use for Object Detection?

The merve/coco2017and merve/pascal-voc datasets can be used for Object Detection.

What metrics can I use for Object Detection?

The Average Precision, Mean Average Precision, and APα metrics can be used for Object Detection.

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},
#...]

Useful Resources

Compatible libraries

Transformers

Transformers.js

Object Detection demo

using facebook/detr-resnet-50

Models for Object Detection

Browse Models (2,913)

facebook/detr-resnet-50

Object Detection • Updated Apr 10 • 440k • • 763

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

jameslahm/yolov10x

Object Detection • Updated Aug 27 • 8.08k • 38

Note Real-time and accurate object detection model.

PekingU/rtdetr_r18vd_coco_o365

Object Detection • Updated Jul 1 • 948 • 3

Note Fast and accurate object detection model trained on COCO and Object365 datasets.

Datasets for Object Detection

Browse Datasets (617)

merve/pascal-voc

Viewer • Updated Jul 6 • 336k • 253

Note Multi-task computer vision benchmark.

Spaces using Object Detection

🏆

hf-vision/object_detection_leaderboard

Note Leaderboard to compare various object detection models across several metrics.

😻

Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS

Note An application that contains various object detection models to try from.

⚡

kadirnar/torchyolo

Note An application that shows multiple cutting edge techniques for object detection and tracking.

🐠

VIPLab/Track-Anything

Note An object tracking, segmentation and inpainting application.

🌖

merve/RT-DETR-tracking-coco

Note Very fast object tracking application based on object detection.

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