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📚 Datature Resources 📚

Open Source Love svg2 PRs Welcome Join Datature Slack MIT license


⚡ A repository of resources used in our tutorials and guides ⚡

👋 Hey there! This library is a collection of useful scripts and tutorials that can be used for integrating with our platform tools on Datature Nexus, or for general Computer Vision application purposes. We also showcase demos of some of the state-of-the-art (SOTA) techniques in areas like Generative AI, Multiple Object Tracking (MOT), and many others.

Made with ❤️ by Datature


📌 Script Categories 📌

📑 Example Scripts

This section contains example scripts that can be used for integrating with our platform tools, or for general CV application purposes.

Topic Description Links
Action Recognition

Action Recognition

For identifying and classifying actions with keypoint detection models.
Read Blog Article
Repo
Active Learning

Active Learning

For performing active learning on your dataset.
Read Blog Article
Open Jupyter Notebook
Open in Google Colab
Documentation
Data Preprocessing

Data Preprocessing

Useful tools for preprocessing your data.
Read Blog Article
Repo
Documentation
Explainable AI

Explainable AI

For interpreting your deep learning model.
Read Blog Article
Open Jupyter Notebook
Open in Google Colab
Generative AI

Generative AI

Synthetic data generation to boost your dataset.
Read Blog Article
Open Jupyter Notebook
Open in Google Colab
arXiv
Inference Dashboard

Inference Dashboard

For easy visualizations of inference results.
Read Blog Article
Repo
Industry Use Cases

Industry Use Cases

For industry-specific use cases.
Read Blog Article Repo
K-Shot Learning

K-Shot Learning

Sample scripts for one-shot and few-shot learning.
Read Blog Article
Repo
Tracking

Tracking

For single and multi-object tracking in videos.
Read Blog Article
Repo

🔨 SDK Guides

PyPI version Downloads Open Jupyter Notebook Open in Google Colab

K-Shot Learning

This section contains guides and code snippets on how to use our Datature Python SDK for automating tasks without having to interact with our Nexus platform. The SDK is available on PyPI. It can be installed by running the following command:

pip install -U datature

The SDK can either be invoked in Python, or through the command line interface (CLI). Installing the pip package will install both the SDK and CLI together. For more information or advanced features on the SDK, please refer to the SDK documentation.


🏭 Deployment

This section contains scripts on how to deploy your models trained on Nexus for inference. We currently support the following deployment methods:

Topic Description Links
Edge Deployment

Edge Deployment

For deploying models on edge devices such as
Raspberry Pi and NVIDIA Jetson Orin.
Read Blog Article
Repo
Documentation
Inference API

Inference API

Where models are hosted on our servers and inference
can be performed through API calls.
Repo
Documentation
Local Inference

Local Inference

For running simple inference scripts
on your local machine.
Repo
Documentation

🚀 Getting Started 🚀

⚙️ Prerequisites

Firstly, users should clone this repository and change to the resource folder directory.

git clone https://github.com/datature/resources.git
cd resources

In each folder, there will be a requirements.txt file that contains the dependencies required for Python scripts to run. Users can install the dependencies by running the following command:

pip install -r requirements.txt

For running Jupyter notebooks locally, users should install Jupyter by running the following command:

pip install notebook

We recommend users to create a virtual environment before installing any dependencies. For more information on virtual environments, please refer to:

🔑 Usage

Each folder contains a README.md file that contains the instructions for running the scripts. Please refer to the README.md file for more information.

✏️ Contributing

Do let us know via an issue if there are any bugs or something is not working, and we will rectify it as soon as we can! Alternatively, you can also submit a pull request with your changes and our team will review it.

We also welcome new contributions to this repository if there are any interesting cutting-edge techniques that we might not have spotted. Please refer to CONTRIBUTING.md for more information on what areas you can contribute in and coding best practice guidelines.

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