Auto Mpg Dataset ((LINK)) Download
Inez Brisker
I'm running into a situation where the stream is exhausted and doesn't "refresh" until I restart the server. If I restart the server I get 23 annotations auto-accepted into the dataset and then a "No Tasks" message. Sometimes I get a duplicate annotation to review before restarting the server but usually not.
Thanks for the detailed report, this is super strange Especially since the auto-accepted examples are added in the stream, as examples are queued up for annotation. So it's no different from any other stream that does stuff within the generator.
I did have auto_count_stream set to true in my prodigy.json! I set it to false but the problem persisted. I then figured I might as well restart the server a bunch of times if it was going to auto-load before accessing the UI to see if I could just add all of the resolved examples on startup. I did this until the count of items added to the db far exceeded the n of non-duplicate annotations, which told me the review recipe wasn't checking against the db for already-existing annotations.
At this point, I decided to power through without --auto-accept, and after a speed run of "a" key tapping things worked as expected (the correct number of resolved gold versions of annotations, the review recipe reporting no more examples to review after we hit that correct number). So-- if someone else hits this issue I'm happy to troubleshoot with them but am going to return to the golden path for now! Thanks for your help.
Oh, this is a good point! The review recipe will exclude from the stream based on what's in the dataset (via Prodigy's default mechanism) but this happens after the stream is set up. And we're applying the auto-adding in the stream, so it definitely needs a check against the hashes in the database so you don't end up with duplicates here. I'll add this fix for the next release
I don't immediately see how this could be related to the issue here, though, but it's still good this came up. If you want to include this update in the meantime, you could open recipes/review.py in your Prodigy installation, find filter_auto_accept_stream and modify it like this:
Glad to hear you found a solution to keep working! If you're able to share your data (existing dataset in the DB + source file), even just privately via email, let me know! Then we can try it out and see if we can reproduce it
I tried using the review recipe on a dataset with conflicting annotations.
With the --auto-accept option on, the only annotations that got added to my new dataset were the once-conflicting-now-resolved annotations. The only way I could get all of the original annotations (including the resolved annotations) in my new dataset was to run the review recipe again without --auto-accept and manually accept each annotation.
Previous investigations into automating urban forest monitoring focused on small datasets from single cities, covering only common categories. To address these shortcomings, we introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery. Our Auto Arborist dataset contains over 2.5M trees and over 300 genera and is more than 2 orders of magnitude larger than the closest dataset in the literature. In our paper we introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
If you are interested in accessing the dataset please fill out the following form. We are releasing the dataset in phases, and we are manually verifying that PII is obscured for all images before release. A data card for our model can be downloaded here.
MIT DriveSeg (Semi-auto) Dataset is a set of forward facing frame-by-frame pixel level semantic labeled dataset (coarsely annotated through a novel semiautomatic annotation approach) captured from moving vehicles driving in a range of real world scenarios drawn from MIT Advanced Vehicle Technology (AVT) Consortium data.
Ding, L., Glazer, M., Terwilliger, J., Reimer, B. & Fridman, L. (2020). MIT DriveSeg (Semi-auto) Dataset: Large-scale Semi-automated Annotation of Semantic Driving Scenes. Massachusetts Institute of Technology AgeLab Technical Report 2020-2, Cambridge, MA. (pdf)
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This dataset was taken from the StatLib library which ismaintained at Carnegie Mellon University. The dataset was used in the1983 American Statistical Association Exposition. The originaldataset has 397 observations, of which 5 have missing values for thevariable "horsepower". These rows are removed here. The originaldataset is avaliable as a CSV file in the docs directory, aswell as at
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The last chart is a feature distance. It measures the similarity between features in a dataset. For example, if twovariables are almost identical, their feature distance will be small. Understanding feature distance is useful in featureselection, where it can be used to identify which variables are redundant and should be considered for removal. Toperform the analysis, we need just one line:
Hi! Thank your for the fast reply. The file metadata_refresh.log is empty. And the command ALTER PDS REFRESH METADATA yes helps, is what I have been doing all the type to refresh it manually. But the same dataset in my old dremio v11.0 does not need me to manually refresh the metadata, why is that? Which setting can be affecting this?
@jbaranda On 23.x by default unlimited splits is turned on and requires your metadata to be moved to S3, In your dremio.conf do you have a dist:/// setting?when this runs via background there should be an internal refresh dataset job created for every PARQUET dataset, can you please find that profile? In addition the ALTER PDS command should also have generated an internal refresh dataset job. Can you please send those 2 profiles for the same dataset?
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EPA requires auto manufacturers to change or update their MPG (miles per gallon) values on fuel economy labels (window stickers) if information comes to light that show that the values are too high. See revisions to fuel economy label estimates.
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The dataset which failed to update contains two data source, SQL Server and Oracle, right? Did you add both two data sources to data Gateway? Also, please check whether the credential you specified when configuring gateway is correct. Besides, what error meesage was prompted?
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