Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on
Slack,
GitHub, Stack Overflow, and Reddit.
Wait, what’s FiftyOne?
FiftyOne is an open source machine learning toolset that enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster.
Ok, let’s dive into this week’s tips and tricks!
Computing embeddings on a PyTorch model
Community Slack member Pedro asked,
“How can I compute embeddings using my PyTorch model? I have trained using ResNet-18 and want to also use it for the embeddings computation. Is this possible?”
FiftyOne provides powerful
embeddings visualization capabilities via the
FiftyOne Brain. With it you can generate low-dimensional representations of the samples and objects in your datasets.
Exporting with absolute paths vs filenames
Community Slack member ZKW asked,
“Is there a method in FiftyOne to add absolute paths in annotation.xml rather than providing a file name?
Yes! You can pass the optional abs_paths=True option to export absolute paths rather than filenames. For example:
Simplest way to get data into the FiftyOne App
Community Slack member J asked,
“What is the simplest way to get example datasets into the FiftyOne App so I can start experimenting with the tool?”
The easiest way to get a dataset into the FiftyOne App is to load the
quickstart dataset which consists of 200 images from the validation split of COCO-2017, with model predictions generated by an out-of-the-box Faster R-CNN model from
torchvision.models.
https://www.youtube.com/watch?v=7mmH-ql_-zg
Alternatively, you can also easily load popular datasets you may already be familiar with from the
FiftyOne Dataset Zoo. For example ActivityNet, COCO, ImageNet, Kinetics, Open Images, and more. Any of these datasets can be loaded (and downloaded if necessary) using the
load_zoo_dataset() command.
Downloading annotated videos stored locally or remotely
Community Slack member Patrick asked,
“Is it possible to download an annotated version of a video from FiftyOne? I have a dataset I created with many videos and their associated object detection predictions. How can I download this annotated video locally?”
FiftyOne supports the annotation of datasets and views via the
draw_labels method in the SDK, and support for this directly in the App is coming very soon! You can use the
export method on your dataset to download your annotated videos. You can perform exports with this method by following the basic patterns detailed below:
- Provide
export_dir and dataset_type to export the content to a directory in the default layout for the specified format, as documented on this page - Provide
dataset_type along with data_path, labels_path, and/or export_media to directly specify where to export the source media and/or labels (if applicable) in your desired format; this syntax provides the flexibility to, for example, perform workflows like labels-only exports - Provide a
dataset_exporter to which to feed samples to perform a fully-customized export
If the dataset is local, pass in your local directory. If the dataset is remote, pass in the directory of your remote machine and then download the video. Here’s an example:
Deleting a batch of samples using a DatasetView
Community Slack member Dan asked,
“How can I delete a group of samples that I have selected with a view?”
You can easily remove a batch of samples from a Dataset by constructing a DatasetView that contains the samples, and then deleting them from the dataset as follows:
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