keremberke/yolov8m-blood-cell-detection
Object Detection
•
Updated
•
4.84k
•
8
Datasets:
Tasks:
Object Detection
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image_id
int64 0
254
| image
imagewidth (px) 416
416
| width
int32 416
416
| height
int32 416
416
| objects
sequence |
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End of preview. Expand
in Dataset Viewer.
Dataset Labels
['platelets', 'rbc', 'wbc']
Number of Images
{'train': 255, 'test': 36, 'valid': 73}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/blood-cell-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3
Citation
@misc{ blood-cell-detection-1ekwu_dataset,
title = { Blood Cell Detection Dataset },
type = { Open Source Dataset },
author = { Team Roboflow },
howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-01-18 },
}
License
Public Domain
Dataset Summary
This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM 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 unstructured image data
- annotate, and create datasets
- export, train, and deploy computer vision models
- use active learning to improve your dataset over time
It includes 364 images. Cells 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 416x416 (Stretch)
No image augmentation techniques were applied.
- Downloads last month
- 217
Size of downloaded dataset files:
4.83 MB
Size of the auto-converted Parquet files:
4.96 MB
Number of rows:
373
Models trained or fine-tuned on keremberke/blood-cell-object-detection