--- task_categories: - image-segmentation tags: - roboflow - roboflow2huggingface ---
triangulum66/bubble_size_distribution
### Dataset Labels ``` ['bubble'] ``` ### Number of Images ```json {'valid': 47, 'test': 32, 'train': 243} ``` ### 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("triangulum66/bubble_size_distribution", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/indian-institute-of-technology-guwahati/project-2-cylun/dataset/2](https://universe.roboflow.com/indian-institute-of-technology-guwahati/project-2-cylun/dataset/2?ref=roboflow2huggingface) ### Citation ``` @misc{ project-2-cylun_dataset, title = { Project 2 Dataset }, type = { Open Source Dataset }, author = { Indian Institute of Technology Guwahati }, howpublished = { \\url{ https://universe.roboflow.com/indian-institute-of-technology-guwahati/project-2-cylun } }, url = { https://universe.roboflow.com/indian-institute-of-technology-guwahati/project-2-cylun }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2024 }, month = { feb }, note = { visited on 2024-02-28 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on February 27, 2024 at 4:50 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 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 322 images. Bubbles are annotated in COCO format. The following pre-processing was applied to each image: No image augmentation techniques were applied.