ImageNet Benchmark Task
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An ImageNet Benchmark Task is a visual entity recognition task supported by an ImageNet database.
- AKA: ImageNet Large Scale Visual Recognition Challenge, ILSVRC.
- Context:
- Project's homepage: http://image-net.org/challenges/LSVRC/
- It benchmarks object detection and image classification systems at large scale.
- It can (typically) be a Large-Scale Data-Driven Task.
- …
- Example(s):
- Counter-Example(s):
- See: Entity Mention Recognition, ImageNet Dataset.
References
2020a
- (ImageNet, 2020) ⇒ http://image-net.org/challenges/LSVRC/ Retrieved: 2020-12-12.
- QUOTE: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. One high level motivation is to allow researchers to compare progress in detection across a wider variety of objects -- taking advantage of the quite expensive labeling effort. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation.
2020b
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/ImageNet Retrieved:2020-9-28.
- The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million[1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories with a typical category, such as "balloon" or "strawberry", consisting of several hundred images.[3] The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes.[4]
- ↑ "New computer vision challenge wants to teach robots to see in 3D". New Scientist. 7 April 2017. Retrieved 3 February 2018.
- ↑ Markoff, John (19 November 2012). "For Web Images, Creating New Technology to Seek and Find". The New York Times. Retrieved 3 February 2018.
- ↑ "From not working to neural networking". The Economist. 25 June 2016. Retrieved 3 February 2018.
- ↑ Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) "ImageNet Large Scale Visual Recognition Challenge". IJCV, 2015
2016
- (ImageNet, 2016) ⇒ http://image-net.org/challenges/LSVRC/
- The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. One high level motivation is to allow researchers to compare progress in detection across a wider variety of objects -- taking advantage of the quite expensive labeling effort. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation.
- The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. One high level motivation is to allow researchers to compare progress in detection across a wider variety of objects -- taking advantage of the quite expensive labeling effort. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation.
2015
- (Russakovsky et al., 2015) ⇒ Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. (2015). “ImageNet Large Scale Visual Recognition Challenge}.” In: International Journal of Computer Vision (IJCV).
2009
- (Deng et al., 2009) ⇒ Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. (2009). “Imagenet: A Large-Scale Hierarchical Image Database.” In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009).