https://github.com/activeloopai/examples

Examples for quickly getting started using Deep Lake! https://activeloop.ai/
https://github.com/activeloopai/examples

deeplake

Last synced: 6 months ago
JSON representation

Examples for quickly getting started using Deep Lake! https://activeloop.ai/

• Host: GitHub
• URL: https://github.com/activeloopai/examples
• Owner: activeloopai
• License: apache-2.0
• Created: 2021-09-22T22:09:56.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-09-04T13:56:38.000Z (about 1 year ago)
• Last Synced: 2024-09-05T17:38:30.367Z (about 1 year ago)
• Topics: deeplake
• Language: Jupyter Notebook
• Homepage: https://docs.activeloop.ai/
• Size: 12.2 MB
• Stars: 23
• Watchers: 4
• Forks: 15
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
[Deprecated] while some tutorials work, they are outdated, would really appreciate contributions to update examples to Deep Lake.



     

    

    
Examples for Deep Lake - Dataset Format for AI

 


A repository showcasing examples of using [Deep Lake](https://github.com/activeloopai/deeplake)

 - [Uploading Dataset Places365](places365/upload.py)

 - [Notebook on uploading COCO](coco/upload_coco.ipynb)

 - [Training a model using Pytorch Lightning](pytorch-lightning/mnist.py)

 - [Augmentation using Albumentations](albumentations/augment.py)

 - [Run Deep Lake on MinIO (local S3)](minio)

 - [Computer Vision Transformation pipeline on Cifar](transforming)

 

### Colab Tutorials

| Notebook    | Link |

|-------------|------|

| Getting Started with Deep Lake  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/main/colabs/Getting_Started_with_Hub.ipynb) |

| Creating Object Detection Datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/main/colabs/Creating_Object_Detection_Datasets.ipynb) |

| Creating Complex Detection Datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/main/colabs/Creating_Complex_Datasets.ipynb) |

| Data Processing Using Parallel Computing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/istranic-adding-colabs/colabs/Data_Processing_Using_Parallel_Computing.ipynb) |

| Training an Image Classification Model in PyTorch  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/main/colabs/Training_an_Image_Classification_Model_in_PyTorch.ipynb) |

| Creating Time-Series Datasets  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/activeloopai/examples/blob/main/colabs/Creating_Time_Series_Datasets.ipynb) |

Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:

 1. [AgriTech](https://www.activeloop.ai/solutions/agriculture/)

 2. [Audio processing](https://www.activeloop.ai/solutions/audio/)

 3. [Autonomous Vehicles & Robotics](https://www.activeloop.ai/solutions/autonomous-vehicles-robotics/)

 4. [Biomedical and Healthcare ML](https://www.activeloop.ai/solutions/biomedical-healthcare/)

 5. Multimedia: [Image enhancement, video enhancement, face detection, sports analytics, or machine learning for AR/VR](https://www.activeloop.ai/solutions/multimedia/)

 6. Safety & Security: [surveillance machine learning with biometrics, facial recognition, or crowd counting](https://www.activeloop.ai/solutions/safety-security/)

## Documentation

Getting started guides, examples, tutorials, API reference, and other usage information can be found on our [documentation page](http://docs.activeloop.ai/?utm_source=github&utm_medium=repo&utm_campaign=readme).