Annotation tools are crucial to the success of machine learning projects, especially in the field of image classification and semantic segmentation.

Luckily, there are several annotation tools that are user-friendly and effective when it comes to annotating data sets and making them actionable by machines.

In this article, we will discuss the top Best Annotation Tools for Machine Learning to help you decide which one best suits your needs.

What are annotations in machine learning?

Annotations are a way of explaining one machine learning concept in terms of another. For example, if you wanted to explain why something is considered spam by a machine learning model, you would need to annotate that spammy feature with information about what makes it spam and how it affects your model.

If you were trying to explain how a semantic segmentation algorithm labels an image, you might want annotations between pixels and words.

These are just two examples of annotations, but there are many different uses for annotations. They help us understand how data flows from one process or dataset into another.

TensorboardX

With TensorboardX, deep learning researchers and students can interactively visualize graphs and data on multiple devices using an intuitive web interface.

Researchers will find its built-in visualization tools – including graph layout and custom node creation – helpful in streamlining deep learning workflows.

It is designed with scalability in mind, so a research team’s entire project can be visualized across many machines at once.

In addition, its customizable tools allow for deeper insights into your datasets to better understand neural network behavior.

For example, you can filter training images by time or region of interest (ROI) during the testing or validation phases of your project.

Furthermore, TensorboardX has been optimized to minimize computation overhead while maximizing performance during a live interaction.

NeurIPSeq

To add annotations, or meaningful tags, to each of your images is known as an image annotation. The process might seem simple but it’s an important part of training machine learning algorithms to understand language.

NeurIPSeq is a java library that has been developed by Stanford University and can be used in a variety of ways.

The code is maintained on Github so you can access it there if you want further clarification, how-to guides, or specific examples.

NeurIPSeq works with TensorFlow but also supports Caffe and MXNet if those libraries are more up your alley.

DeepChem

For semantic segmentation and object detection in general, we like DeepChem. It offers a clean user interface as well as a Python API.

In addition, it has recently added training capabilities that make DeepChem one of our favorite tools for building machine learning models—including those used in industrial applications.

It is also free and open-source, making it an affordable solution for anyone looking to get into image annotation at scale or build on what they already have with pre-existing annotations.

There are a number of paid services, like Clarifai, that provide even more tools and applications, so it’s worth taking a look at them as well.

For example, Clarifai offers an object detector called ObjectNet that is trained on multiple object categories including clothing and gadgets.

Chainer NNVM

NNVM is a Python package that eases neural network research by allowing users to easily define and experiment with many types of neural networks.

It provides support for training, evaluation, and visualization of different kinds of neural networks with minimal effort.

Currently, it supports feed-forward neural networks such as Multilayer Perceptron, Convolutional Neural Network, Recurrent Neural Network (RNN), Autoencoder, etc. as well as recurrent NN models such as LSTM and GRU.

These models can be trained using parallel threads on multiple GPUs/CPUs in order to speed up computation time significantly.

NNVM also supports Neural Architecture Search, a state-of-the-art method of training several neural networks with different parameters and then picking out which ones work best by evaluating them on test data.

PaddlePaddle

It’s currently one of my favorite ML frameworks because it uses dynamic neural networks, making it easy to use.

PaddlePaddle is particularly adept at processing lots of data and very large model sizes (that’s called scalability) and comes with a lot of built-in models.

You can use pre-trained PaddlePaddle models or download them from their website. For example, if you want to do semantic segmentation on images and video, I recommend taking a look at PaddleVision.

The most complete implementation of our VGG network that is available today was trained using the ImageNet dataset by Alex Krizhevsky et al.

JupyterLab

There are three main tasks in building neural networks: encoding data, training models, and making predictions.

JupyterLab is a new addition to Python’s ecosystem that handles all of these things. It’s a web-based tool that you can use on its own or with any other Jupyter notebooks or documents.

It offers a graphical user interface with many shortcuts, letting you quickly construct and visualize models in an intuitive way.

You can also export your work as interactive slideshows and PDF reports—making it ideal for sharing your findings with clients or colleagues from different fields.

Simply put, if you do any machine learning at all, JupyterLab should be your first stop in creating new projects.

iPython (Jupyter) Notebook

Jupyter notebooks are a common way to create and share documents that contain live code, equations, visualizations, and narrative text.

They’re an open-source project by Continuum Analytics. While they originated as a Python tool, they’ve grown popular among other communities, including R and Julia developers.

You can think of them as a notebook metaphor—much like a writer might use paper and pen—for coding.

iPython notebooks allow you to embed, execute and interleave code (both Python and R) with natural language text.

Free slide

One of FreeSlide’s main selling points is that you can use any image from Unsplash or Flickr, which could be a plus or minus depending on your intended use.

Using them makes sense if you plan on doing a lot of data science work, but it also means you’ll have fewer options if you want more specific pictures.

Once you’ve selected an image, there are sliders that allow you to define how visible annotations will be and how easy they will be to select.

It also has presets for different kinds of projects: If your aim is something like classification tasks, Freeslide can make sure there’s plenty of overlap in your data.


Chris A.

Chris is a profound internet and Digital marketer who loves to find solutions for all your queries and doubts on tech and marketing likewise.

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