Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Sub-tasks:
multi-label-image-classification
Languages:
English
Size:
< 1K
Tags:
golf-courses
License:
File size: 5,372 Bytes
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---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: bethecloud/golf-courses
size_categories:
- n<1K
source_datasets: []
tags:
- golf-courses
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
---
## Dataset Description
- **Homepage: https://mirror.xyz/bitkevin.eth**
- **Repository: https://colab.research.google.com/drive/1EnqpDiKOVYhR0c6f4CgmDg2zqcbYZJpB#scrollTo=c1ef3d21-6e0e-46c9-a459-8a2ab856a5ca**
- **Point of Contact: Kevin Leffew – [email protected]**
### Dataset Summary: golf-course
This dataset (bethecloud/golf-courses) includes 21 unique images of golf courses pulled from Unsplash.
The dataset is a collection of photographs taken at various golf courses around the world. The images depict a variety of scenes, including fairways, greens, bunkers, water hazards, and clubhouse facilities. The images are high resolution and have been carefully selected to provide a diverse range of visual content for fine-tuning a machine learning model.
The dataset is intended to be used in the context of the Hugging Face Dream Booth hackathon, a competition that challenges participants to build innovative applications using the Hugging Face transformers library. The submission is for the category of landscape.
Overall, this dataset provides a rich source of visual data for machine learning models looking to understand and classify elements of golf courses. Its diverse range of images and high-quality resolution make it well-suited for use in fine-tuning models for tasks such as image classification, object detection, and image segmentation.
By using the golf course images as part of their training data, participants can fine-tune their models to recognize and classify specific features and elements commonly found on golf courses. The ultimate goal after the hackathon is to pull this dataset from decentralized cloud storage (like Storj DCS), increasing its accessibility, performance, and resilience by distributing across an edge of over 17,000 uncorrelated participants.
## Example Output
![golf-acropolis.jpg]https://link.storjshare.io/juid5vc27dbajh6zyzplf4fah5xq/golf-course-output%2Fgolf-acropolis.png
# Usage
The golf-courses dataset can be used by modifying the instance_prompt: a photo of golf course
### Languages
The language data in golf-courses is in English (BCP-47 en)
## Dataset Structure
The complete dataset is GBs and consists of 21 objects.
### Parallelized download using Decentralized Object Storage (Storj DCS)
A direct download for the dataset is located at https://link.storjshare.io/juo7ynuvpe5svxj3hh454v6fnhba/golf-courses.
In the future, Storj DCS will be used to download large datasets (exceeding 1TB) in a highly parallel, highly performant, and highly economical manner (by utilizing a network of over 17,000 diverse and economically incentivized datacenter node endpoints.
### Curation Rationale
This model was created as a sample by Kevin Leffew as part of the DreamBooth Hackathon.
### Source Data
The source data for the dataset is simply pulled from Unsplash
### Licensing Information
MIT License
## Thanks to John Whitaker and Lewis Tunstall
Thanks to [John Whitaker](https://github.com/johnowhitaker) and [Lewis Tunstall](https://github.com/lewtun)for writing out and describing the initial hackathon parameters at https://huggingface.co/dreambooth-hackathon.
## Example Training Data
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