sceneparsing.csail.mit.edu

MIT Scene Parsing Benchmark

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset. The data in the benchmark has been used in the Scene Parsing Challenge 2016. Held jointly with ILSVRC'16. Demo of scene parsing is available. Data, models, toolkit. To pre...

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MIT Scene Parsing Benchmark | sceneparsing.csail.mit.edu Reviews

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Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset. The data in the benchmark has been used in the Scene Parsing Challenge 2016. Held jointly with ILSVRC'16. Demo of scene parsing is available. Data, models, toolkit. To pre...

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1 overview

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3 evaluation

4 previous challenges

5 citation

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7 data train/val 922mb

8 pretrained models caffemodels

9 code

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MIT Scene Parsing Benchmark | sceneparsing.csail.mit.edu Reviews

https://sceneparsing.csail.mit.edu

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset. The data in the benchmark has been used in the Scene Parsing Challenge 2016. Held jointly with ILSVRC'16. Demo of scene parsing is available. Data, models, toolkit. To pre...

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sceneparsing.csail.mit.edu

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MIT Scene Parsing Dataset

http://sceneparsing.csail.mit.edu/eval

This is the evaluation server for the MIT Scene Parsing Benchmark. The images in the test set could be downloaded from the Project Page. You could log in to submit your prediction result for the test set or register a new user on the left panel. The leaderboard shows the rank of all the submissions.

2

MIT Scene Parsing Challenge 2016

http://sceneparsing.csail.mit.edu/index_challenge.html

The goal of this challenge is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. The data for this challenge comes from ADE20K Dataset. This scene parsing challenge is held jointly with ILSVRC'16. Winners will be invited to present at ILSVRC and COCO joint workshop at ECCV 2016. Take a look at the relevant challenge Places2 Scene Recognition 2016. Demo of scene parsing is available. June 22, 2016. Sep 4, 2016. Sep 26, 2016.

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Index of /model

http://sceneparsing.csail.mit.edu/model

DilatedNet iter 120000.caffemodel. FCN iter 160000.caffemodel. Apache/2.2.22 (Ubuntu) Server at sceneparsing.csail.mit.edu Port 80.

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Antonio Torralba

http://web.mit.edu/torralba/www

Computer Science and Artificial Intelligence Laboratory. Dept of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Address: 32 Vassar Street, Cambridge, MA 02139. My research is in the areas of computer vision, machine learning and human visual perception. I am interested in scene and object recognition, among other things. A scene parsing challenge is being held jointly with ILSVRC'16. Aligning books and movies. Places2 scene classification challenge. For more details o...

places.csail.mit.edu

MIT Places Database for Scene Recognition

http://places.csail.mit.edu/index.html

By MIT Computer Science and Artificial Intelligence Laboratory. Scene Parsing Challenge 2016. Is online. Welcome to participate. Is coming at ECCV'16. We release the CNNs trained on Places365 (new Places2 data). With more categories predicted and better performance. Places205-VGG and Places205-GoogLeNet are available to download in the Places CNNs. Register to download data and submit prediction results at here. The leaderboard of Places Database is at here. Sample Code of Unit Segmentation. B Zhou, A&#4...

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Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset. The data in the benchmark has been used in the Scene Parsing Challenge 2016. Held jointly with ILSVRC'16. Demo of scene parsing is available. Data, models, toolkit. To pre...

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