# blood-cell-object-detection **Repository Path**: hf-datasets/blood-cell-object-detection ## Basic Information - **Project Name**: blood-cell-object-detection - **Description**: Mirror of https://huggingface.co/datasets/keremberke/blood-cell-object-detection - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-10-30 - **Last Updated**: 2024-06-09 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README --- task_categories: - object-detection tags: - roboflow - roboflow2huggingface - Biology ---
keremberke/blood-cell-object-detection
### Dataset Labels ``` ['platelets', 'rbc', 'wbc'] ``` ### Number of Images ```json {'train': 255, 'test': 36, 'valid': 73} ``` ### 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("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](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface) ### 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.