Difference between revisions of "YOLO"
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{{Technology | {{Technology | ||
| − | |Developped by (institutions)=( | + | |Developped by (institutions)=University of Washington, Allen Institute for Artificial Intelligence, Facebook AI Research |
| − | |Creation Date= | + | |Developped by (people)=Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi |
| + | |Creation Date=2016 | ||
|Technology Type=Object Detection | |Technology Type=Object Detection | ||
}} | }} | ||
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<blockquote>We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. | <blockquote>We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. | ||
| − | Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset. | + | Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including [[Related Technologies::DPM]] and [[Related Technologies::R-CNN]], by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset. |
</blockquote> | </blockquote> | ||
Revision as of 09:33, 17 May 2021
Technology Type: Object Detection
Details:
| Dataset used | |
|---|---|
| Related Technologies | DPM R-CNN Darknet |
| Code Repository |
Developers and Users:
| Developed by | University of Washington |
|---|
Events:
| Creation Date | 2016 |
|---|---|
| End Date |
Description[ ]
- Localisation
- Classification
(You Only Look Once), is a network for object detection. The object detection task consists in determining the location on the image where certain objects are present, as well as classifying those objects.
Original Paper: 1
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.
Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.
References
- ^ redmonYouOnlyLook2016