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Feb 23, 2022 - Droneways
Unknown - 8.7 MB - MD5: 7fbed4d86b045fe0716ae78b4467b9ed
Feb 23, 2022 - Droneways
Unknown - 13.4 MB - MD5: b29cd530f2e6ea428da491680fb6edae
Feb 23, 2022 - Droneways
XML - 2.2 KB - MD5: 688978585ebc3700a696aa71e90122f7
Feb 23, 2022 - Droneways
Unknown - 5.1 KB - MD5: fed6a994ebaa429466483610d8a3226d
Feb 23, 2022 - Droneways
Plain Text - 1.5 KB - MD5: 21ac067316c302abd7e53656f007a9b1
Feb 23, 2022 - Droneways
TAR Archive - 1.2 GB - MD5: 5c27846cc04f367049d079d8090b992d
Feb 23, 2022 - Droneways
Python Source Code - 3.8 KB - MD5: 0ce7585c3af0830003a83a186decc6d9
Feb 23, 2022 - Droneways
Unknown - 1.8 MB - MD5: 3a01313304e03d6d349ebf310f0a7e8e
Jan 27, 2022
Abreu, Fernando Ferreira; Rodrigues, Luiz Henrique Antunes, 2022, "MTIL - Mini tomato image library", https://doi.org/10.25824/redu/3CP9NK, Repositório de Dados de Pesquisa da Unicamp, V1
1. What's MTIL? MTIL - Mini Tomato Image Library is a dataset with images of Mila c.v, taken in a greenhouse in Agricultural Engineering School at University of Campinas, Brazil. 2. What's it's purpose? It was create to be used in deep learning segmentation tasks. 3. Where's this...
Unknown - 351.7 MB - MD5: 331293fbdbf41e88febbe231b4cf745e
Inside this zip file there are two subdirectories "clipped" and "masks" as described previously. Just rename the file from "zipfile" to "zip" and uncompress it.
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