NInico401·Feb 21, 2023Submission
Lion, a new Optimizer from Google, provides 3-5x speedup compared to AdamW
1 points · Open discussiongoogle
automl
Jupyter Notebook2020–2023
google / automl · Archived
automl
Google Brain AutoML
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6.5KGitHub stars
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.
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HN mentions by year5 dated mentions
Observed mentions: 2020-03-19 17:00:58 UTC to 2023-02-21 16:12:39 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 3 | 2 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
5 sourcesCode for EfficientNetV2 (Deep Learning model) released
2 points · Open discussionThere seems to be an unfair comparison between the various network architectures. The reported speed and accuracy improvements should be taken with a bit of scepticism for two reasons.
* This is the first yolo implemented in Pytorch. Pytorch is the fastest ml framework around, so some of YOLOv5's speed improvements may be attributed to the platform it was implemented on rather than actual scientific advances. Previous yolos were implemented using darknet, and EfficientDet is implemented in TensorFlow. It would be necessary to train them all on the same platform for a fair speed comparison.
* EfficientDet was trained on the 90-class COCO challenge (1), while YOLOv5 was trained on 80 classes (2).
[1] https://github.com/ultralytics/yolov5/blob/master/data/coco....
[2] https://github.com/google/automl/blob/master/efficientdet/in...
In YOLOv5: State-of-the-art object detection at 140 FPS