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In Praise of Idleness, Bertrand Russel


unspun | Swift Engineer | San Francisco Bay Area We built an automated process for creating perfectly fitting jeans. Starting from a customer’s 3D body scan, our system generates customized pattern pieces that are then assembled on-demand. This removes the need for inventory and eliminates unnecessary waste.

We're looking for a swift dev to join the small software team.

Job Description: https://angel.co/company/unspun/jobs/1460386-ios-engineer

email: [email protected]


unspun | Swift Engineer | San Francisco Bay Area

We built an automated process for creating perfectly fitting jeans. Starting from a customer’s 3D body scan, our system generates customized pattern pieces that are then assembled on-demand. This removes the need for inventory and eliminates unnecessary waste.

We're looking for a swift dev to join the small software team.

Job Description: https://angel.co/company/unspun/jobs/1460386-ios-engineer

email: [email protected]


It's too soon.


This quote.

> We worked at a startup that leveraged autonomous blockchains to transfer money from naïve investors to slightly less naïve twenty-somethings. There are worse gigs.


There 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...


Great points, and hoping Glenn releases a paper to complement performance. We are also planning more rigorous benchmarking nonetheless.

re: PyTorch being a confounding factor for speed - we recompiled YOLOv4 to PyTorch to achieve 50 FPS. Darknet would likely top out around 10 FPS on the same hardware.

EDIT: Alexey, author of YOLOv4, provided benchmarks of YOLOv4 hitting much higher FPS here: https://github.com/AlexeyAB/darknet/issues/5920#issuecomment...


Side note: I like Pytorch but eager pytorch is not faster the jax.jit or tf.function code


I had no idea which parts of this website were real.


Reading HN, Smashing Magazine, and A List Apart will pay off greatly in a few years.


You mostly have a presentation problem if you have marketable skills that won't sell. I started when I was 15 and the trick was displaying confidence, humility, and maturity in every interaction. It's harder than it sounds.


Life Work by Donald Hall


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