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Is there a way to apply a function to all samples in a dataset? Something equivalent to [some_func(x.gt_classifications.classifications) for x in some_dataset.samples], but hopefully a lot faster?values() function should speed things up considerably. use values() extracts the slice of data you wish to modify and then set_values() saves the updated data in a single batch operation. For example:I am running inference over my image dataset. I am using a simple approach:
My images are stored in a remote server so fetching the images takes a considerable amount of time. The iteration speed is around 5 samples per second which is very slow for my purposes. Is there any way to parallelise this?
torch Dataset and Dataloader, where the latter can be parallelized and yield single images or batches. The FiftyOne GitHub repo has some utilities for out of the box use, but you can also easily customize them for your specific needs.yolo model, you may want to consider registering your model with the zoo and using FiftyOne’s apply_model or leveraging FiftyOne’s native Ultralytics integration.sample.save() calls which can create a bottleneck. You can try iter_samples(autosave=True) when making sample modifications. This will efficiently batch sample saves.Is there a way to filter labels, but keep samples that don’t match a specific annotation? For example, I have a bunch of street images with cars and people. I want to see a view of the dataset that contains all the images, except for cars and people.
Let’s assume I have a dataset, with two saved viewsaandb, with one or more view stages each. Is there a way to get back the history of view stages from each view? If yes, is it possible to combine them, e.g. get the union or intersection?
view._stages to see the list of stages that comprise each view and the kwargs used to define them. Also, you can learn more about how to concatenate views here.How can I disable the outputs displayed when loading datasets in a Jupyter notebook? For example:
fo.config.show_progress_bars=False
