PRprakashqwerty·Jul 12, 2022Submission
Feature request: add PR curve in TensorFlow object detection API / eval.p
1 points · Open discussiontensorflow
models
Python2016–2022
tensorflow / models
models
Models and examples built with TensorFlow
60Distinct people
65Mentions
0Explicit recommenders
77.6KGitHub stars
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Models and examples built with TensorFlow
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- bazelbuild/bazel · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 3:30:57 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year65 dated mentions
Observed mentions: 2016-03-09 20:36:16 UTC to 2022-07-12 14:14:04 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 16 | 4 | 12 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 25 | 2 | 23 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 12 | 4 | 8 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 3 | 2 | 1 |
What Hacker News says
3 sourcesGoogle Photos is the wrong product to ask this question, IMHO. One of its key highlight is sharing albums with others and preserving them for the future, so why waste a consumer device's energy on unnecessary tasks if the image is going to end in a Google datacenter anyway?
Google Lens, which isn't about sharing, does run many functions on device: for example, it does ondevice offline translation: https://9to5google.com/2021/01/25/google-lens-translate-offl...
The general direction at Google (https://ai.googleblog.com/2021/11/improved-on-device-ml-on-p...) is to move as much as possible on device for privacy and latency reasons.
There are published and opensource (Apache license, according to the github LICENSE file) models for Tensorflow Lite which runs on Pixel Edge TPUs (and most embedded devices):
https://tfhub.dev/s?q=edgetpu
https:/
In Deploying Transformers on the Apple Neural EngineShow HN: Faster R-CNN object detector implemented in PyTorch and TensorFlow 2
Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras.
I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from.
I also wanted to solidify my understanding of TensorFlow/Keras and learn PyTorch, so I made sure to implement the model in both. Faster R-CNN is fairly challenging in that it doesn't quite map to Keras tutorial examples. For example, losses are not computed simply as a function of model output and input but rat
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her, some of the training data is actually computed on-the-fly within the model during training and the losses have to explicitly be constructed as part of the graph.
Personally, I found the "official" TensorFlow reference implementation (https://github.com/tensorflow/models/blob/master/research/ob...) to be very difficult to follow and I hope this proves to be useful to learners like myself.