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Integration:  dvc git mlflow
Jinen Setpal 68a54c3bdc
picked up dependencies from ipynb
1 year ago
0f2513d300
Prettify notebook and finishing touches to make the project work for new users through the webinar
2 years ago
dda98b8c14
Treat another edge case with image URI
1 year ago
0f2513d300
Prettify notebook and finishing touches to make the project work for new users through the webinar
2 years ago
68a54c3bdc
picked up dependencies from ipynb
1 year ago
2235731fd0
Add COCO_1K Dataset
2 years ago
0f2513d300
Prettify notebook and finishing touches to make the project work for new users through the webinar
2 years ago
0f2513d300
Prettify notebook and finishing touches to make the project work for new users through the webinar
2 years ago
0f2513d300
Prettify notebook and finishing touches to make the project work for new users through the webinar
2 years ago
b2c3a06409
Upload notebook after initial training
2 years ago
f14ee63395
Add new data
2 years ago
e1071ba562
fixed registry, updated requirements, made notebook more follow-able on colab
1 year ago
e1071ba562
fixed registry, updated requirements, made notebook more follow-able on colab
1 year ago
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README.md

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COCO 1K

A Dataset of ~1,199 images (1,100 training and 99 validation) from the COCO-2017 dataset. Created for the purposes of fast dataset iteration and prototyping. Demos the usage of DagsHub Data Engine which enables you to iterate on your datasets quickly.

Data Engine Flow

To use with the DagsHub Data Engine:

pip install dagshub

And then in your Python:

from dagshub.data_engine import datasources

ds = datasources.get('Dean/COCO_1K', 'COCO_1K')

ds.head().dataframe

To learn more about Data Engine, check out the Docs

You can use the included Notebook to create an end to end flow of the Data Engine, which will enable you to create the datasource, query it, visualize it, train a model, add new data and annotate it using a machine learning model backend, and improve the model performance by improving the data quality.

Tip!

Press p or to see the previous file or, n or to see the next file

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1000 Images from COCO dataset with polygon segmentation

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