datumaro
GeoCOCO
| datumaro | GeoCOCO | |
|---|---|---|
| 2 | 1 | |
| 486 | 4 | |
| 2.5% | - | |
| 9.4 | 8.8 | |
| 3 days ago | 7 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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datumaro
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Does anyone use CVAT for image annotation?
1) CVAT has internal inference for models. If you upload model there in the correct format, then it will be able to generate the detection box itself - https://onepanel.medium.com/train-an-object-detection-model-from-scratch-and-run-inference-on-it-in-10-minutes-16147ef656aa 2) Yes you can upload your prediction. But last time i did it - there were some problems and it took me several hours. It seems to me that you just need to load the markup in one of the formats that it supported by CVAT. If your format is not supported, then you will need to convert. For example like this - https://github.com/openvinotoolkit/datumaro
- Can I know what kind of dataset annotation for object detection is this?
GeoCOCO
What are some alternatives?
cocojson - Utility scripts for COCO json annotation format
qgis-earthengine-examples - A collection of 300+ Python examples for using Google Earth Engine in QGIS
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
WhiteboxTools-ArcGIS - ArcGIS Python Toolbox for WhiteboxTools
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
DA-RetinaNet - Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
UCF-SST-CitySim1-Dataset - Official github page of UCF SST CitySim Dataset
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/opencv/cvat]