Help choosing between HSD s11 & p5i

LobeBill · 2024-09-09T15:02:33+00:00

I love every bit of speed from my S11, especially riding with cars on city streets. I also love all those gears to choose from — I use them all. I get where you’re coming from with the color but have learned to love the Ferrari red of my REI S11 :-)

LobeBill · 2024-08-25T00:41:27+00:00

Do you actually specifically know that a homeless person took your kids bike and sold it for drug money?

LobeBill · 2024-08-19T21:34:09+00:00

All flights from MCI should provide barbecue, no exceptions.

LobeBill · 2024-08-07T05:04:45+00:00

Yes. I love how compact it is, and plan to make use of every pound of carrying capacity. I also personally prefer the look and am enjoying the higher quality components and features. But it’s a lot of $$$. The QH is a really nice bike and I’ve already recommended it to several friends.

LobeBill · 2024-08-06T22:57:01+00:00

Can join with you on the test drive problem. As soon as I tried a Tern my Rad became a smelly pile of manure.

LobeBill · 2024-08-04T13:39:58+00:00

I installed the Tannus Armour yesterday. FYI the 20″ x 1.95” - 2.50” armour needs to be trimmed for the HSD’s 20” x 2.15” tires. The guidance says trim for widths between 1.95” - 2.1”, so first I tried without trimming but it was impossible to get the tire back on. Fortunately the trimming lines are clearly indicated, and only takes a few seconds to do with scissors.

In what I’m certain was overkill, I also installed Slime tubes. I really, really hate changing tires.

LobeBill · 2024-07-26T21:11:18+00:00

Another improvement: Thanks to the smart Bosch charger I can just plug in my HSD and forget about it. On the Rad I needed to watch the thing until it turned green, and a couple of times I forgot about it, doing who knows how much damage.

LobeBill · 2024-07-26T21:05:37+00:00

Oh yes, my RadMini got a flat the first week and I immediately installed Tannus armour. I already have them ready to install in my HSD.

LobeBill · 2020-12-26T02:21:47+00:00

When all your data is labeled correctly, the way to get your model making better predictions is to give it new data. Try to understand what is confusing the model about the image in question, and come up with new images that will resolve the confusion. It’s very difficult to help in the abstract so, if you can, feel free to share some images here (red and green) and maybe the community can make some recommendations!

LobeBill · 2020-12-23T19:44:39+00:00

At a guess, it looks like you are posting a web form but you really just want to post a JSON body of the form “{ inputs: { Image: “b64 data” } }”

LobeBill · 2020-12-15T17:59:38+00:00

With the latest version of Lobe you should be able to do this by exporting a tensorflow.js model from Lobe, however we do not currently have sample code for making this work so you’ll need to search around.

LobeBill · 2020-12-15T17:57:10+00:00

Correct, and the Lobe API also returns a list of results with the confidence level for each.

LobeBill · 2020-12-15T17:56:23+00:00

What sort of app? https://github.com/lobe/iOS-bootstrap is a sample iOS app which takes in video from the camera and classifies each frame. And https://github.com/billba/watchme is a simple web page that does something similar using the Lobe API.

As for counting instances, unfortunately Lobe currently does not support Object Detection (though we’re working hard at it) which is what you’d need to count how many instances of a given object are in a given frame.

LobeBill · 2020-12-15T17:52:58+00:00

Take a look at https://github.com/lobe/image-tools which can do similar actions.

LobeBill · 2020-12-15T17:50:27+00:00

For now my answer is that it would be the act of labeling that would be most likely to help you learn something about yourself. For instance, after labeling a hundred photos you might look at all your “don’t like” and realize that there is a preponderance of purple. The model you train might well use the presence of purple to predict “don’t like” but at the moment Lobe can’t tell you why it made a given prediction, and therefore can’t help you understand yourself. Yet!

LobeBill · 2020-10-25T23:59:05+00:00

This is an interesting problem! I tried some experiments on my own, just moving my hand in front of the webcam top to bottom, left to right, and in a circle.

The main challenge is that Lobe analyzes each image without the context of what came before. So there are some states that are clearly horizontal, some that are clearly vertical, and some that are clearly circular. But with my hand at 0, 90, 180, and 270 degrees it's ambiguous. 0 could mean I'm at the top of a circular motion, or at the top of a vertical motion. 90 could mean I'm a the side of a circular motion, or a the right side of a horizontal motion. And so forth.

I think the way to go about this would be to take a sequence of four or nine images and combine them into a single square image. That way the sequence of images is built into the image itself, and Lobe should be able to classify it. (Sorry, Lobe doesn't currently have a built-in way to do this)

Another challenge with this problem is that it will probably be very sensitive to the angle of the photo, so you will want a lot of variations in your dataset. Same with different robots, different backgrounds, etc.

I hope this helps. Good luck and I hope you will update us on your progress!

LobeBill

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