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Merge pull request activeloopai#1551 from activeloopai/revert-1544-is…
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Revert "Updated formatting of features in readme"
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tatevikh authored Mar 16, 2022
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Expand Up @@ -31,20 +31,13 @@ Hub is a dataset format with a simple API for creating, storing, and collaborati

Hub includes the following features:

### **Storage agnostic API**
* Use the same API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, local storage, and in-memory.
### **Compressed storage**
* Store images, audios and videos in their native compression, decompressing them only when needed, e.g., when training a model.
### **Lazy NumPy-like slicing**
* Treat your S3 or GCP datasets as if they are a collection of NumPy arrays in your system's memory. Slice, index, or iterate through them. Only the bytes you ask for will be downloaded!
### **Dataset version control**
* Commits, branches, checkout - Concepts you are already familiar with in your code can now be applied to your datasets.
### **Third-party integrations**
* Hub comes with built-in integrations for Pytorch and Tensorflow. Train your model with a few lines of code - we even take care of dataset shuffling. :)
### **Distributed transformations**
* Rapidly apply transformations on your datasets using multi-threading, multi-processing, or our built-in [Ray](https://www.ray.io/) integration.
### **Instant visualization**
* Hub datasets are instantly visualized with bounding boxes, masks, annotations, and more in [Activeloop Platform](https://app.activeloop.ai/?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readme) (see below).
* **Storage agnostic API**: Use the same API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, local storage, as well as in-memory.
* **Compressed storage**: Store images, audios and videos in their native compression, decompressing them only when needed, for e.g., when training a model.
* **Lazy NumPy-like slicing**: Treat your S3 or GCP datasets as if they are a collection of NumPy arrays in your system's memory. Slice them, index them, or iterate through them. Only the bytes you ask for will be downloaded!
* **Dataset version control**: Commits, branches, checkout - Concepts you are already familiar with in your code repositories can now be applied to your datasets as well.
* **Third-party integrations**: Hub comes with built-in integrations for Pytorch and Tensorflow. Train your model with a few lines of code - we even take care of dataset shuffling. :)
* **Distributed transforms**: Rapidly apply transformations on your datasets using multi-threading, multi-processing, or our built-in [Ray](https://www.ray.io/) integration.
* **Instant visualization support**: Hub datasets are instantly visualized with bounding boxes, masks, annotations, etc. in [Activeloop Platform](https://app.activeloop.ai/?utm_source=github&utm_medium=github&utm_campaign=github_readme&utm_id=readme) (see below).

<div align="center">
<a href="https://www.linkpicture.com/view.php?img=LPic61b13e5c1c539681810493"><img src="https://www.linkpicture.com/q/ReadMe.gif" type="image"></a>
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