vaguelan/soy-segment

Dataset Labels

['caruru_weed', 'grassy_weed', 'soy_plant']

Number of Images

{'valid': 161, 'test': 181, 'train': 658}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("vaguelan/soy-segment", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/weeds-d0as0/soy-weed-seg/dataset/4

Citation

@misc{
                            soy-weed-seg_dataset,
                            title = { soy-weed-seg Dataset },
                            type = { Open Source Dataset },
                            author = { weeds },
                            howpublished = { \\url{ https://universe.roboflow.com/weeds-d0as0/soy-weed-seg } },
                            url = { https://universe.roboflow.com/weeds-d0as0/soy-weed-seg },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { nov },
                            note = { visited on 2024-07-16 },
                            }

License

MIT

Dataset Summary

This dataset was exported via roboflow.com on July 16, 2024 at 6:30 AM GMT

Roboflow is an end-to-end computer vision platform that helps you

• collaborate with your team on computer vision projects
• collect & organize images
• understand and search unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 1000 images. Soy, weeds are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)
• Resize to 640x640 (Stretch)

No image augmentation techniques were applied.