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---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="vaguelan/soy-segment" src="https://huggingface.co/datasets/vaguelan/soy-segment/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['caruru_weed', 'grassy_weed', 'soy_plant']
```
### Number of Images
```json
{'valid': 161, 'test': 181, 'train': 658}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
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](https://universe.roboflow.com/weeds-d0as0/soy-weed-seg/dataset/4?ref=roboflow2huggingface)
### 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.
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