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Publication Year: 2023

11,661 to 11,670 of 12,159 Results

20221230_HOP_REDOBS_glo.txt Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022 Plain Text - 2.2 MB - MD5: 979c27e6937be8e541d56e7ef0f4273b
20221230_HOP_REDOBS_gps.txt Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022 Plain Text - 2.2 MB - MD5: f337a778a15bfafb82663fc8f10e8443
20221231_HOP_REDOBS_gal.txt Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022 Plain Text - 3.5 MB - MD5: ceef809978b17a510bd04178cb6fe000
20221231_HOP_REDOBS_glo.txt Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022 Plain Text - 2.3 MB - MD5: 9646745f32dd35a3853d1b3b1bf53ef5
20221231_HOP_REDOBS_gps.txt Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022 Plain Text - 2.2 MB - MD5: 8d272d80b898fd6cb6b5ea237fd07ce5
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Mar 29, 2023 - UiT The Arctic University of Norway Gupta, Deepak K.; Bhamba, Udbhav; Thakur, Abhishek; Gupta, Akash; Sharan, Suraj; Demir, Ertugrul; Prasad, Dilip K., 2023, "Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images", https://doi.org/10.18710/4F4KJS, DataverseNO, V1 Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and th...
00_ReadMe.txt Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Plain Text - 6.2 KB - MD5: 66b4fc3733f5c54175c57f7c40d24869
test.zip Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images ZIP Archive - 8.7 GB - MD5: 776f8bccbb1285032864afee9cfa991b
train.csv Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Comma Separated Values - 373.1 KB - MD5: acd730388d00a1102f17a8139106ac42 For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no
train.zip Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images ZIP Archive - 8.8 GB - MD5: d6b3f3ca33e41e006e258f93704417e2 The training files containing all the images of the training set.

20221230_HOP_REDOBS_glo.txt

Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022

Plain Text - 2.2 MB -

20221230_HOP_REDOBS_gps.txt

Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022

Plain Text - 2.2 MB -

20221231_HOP_REDOBS_gal.txt

Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022

Plain Text - 3.5 MB -

20221231_HOP_REDOBS_glo.txt

Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022

Plain Text - 2.3 MB -

20221231_HOP_REDOBS_gps.txt

Mar 30, 2023 - GNSS Scintillation Data (60 s) at Hopen in 2022

Plain Text - 2.2 MB -

Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Mar 29, 2023 - UiT The Arctic University of Norway

Gupta, Deepak K.; Bhamba, Udbhav; Thakur, Abhishek; Gupta, Akash; Sharan, Suraj; Demir, Ertugrul; Prasad, Dilip K., 2023, "Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images", https://doi.org/10.18710/4F4KJS, DataverseNO, V1

Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and th...

00_ReadMe.txt

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Plain Text - 6.2 KB -

test.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.7 GB -

train.csv

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Comma Separated Values - 373.1 KB -

For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no

train.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.8 GB -

The training files containing all the images of the training set.

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