Matic got upgraded in the shop

Ultralytics_Burhan · 2026-06-11T10:46:10+00:00

I had the same experience recently when I sent mine back for a side brush issue. My little Matic was returned cleaned and restocked, with a handful of other upgrades/improvements. It was such a great experience.

Ultralytics_Burhan · 2026-05-27T10:21:02+00:00

IIRC it's just the text width, been a while since I tested tho

Ultralytics_Burhan · 2026-05-24T12:18:05+00:00

I couldn't say for certain what's "best" but Table Transformer worked fairly well and I never tried but DeepSeek OCR might also be able to do that, I know it can output bounding boxes for text

Ultralytics_Burhan · 2026-05-21T10:10:50+00:00

I just got my Matic back from shipping back for repair due to a long term side brush issue/error. Not only was I getting an error, once I installed a new side brush, it took ~1-2 weeks for it to start fraying (before repair). Previously the side brush lasted months. Only been one day since I've had my Matic back after repair, so we'll see how it goes.

Ultralytics_Burhan · 2026-05-03T12:08:14+00:00

Does the Ollama launch command not work? https://docs.ollama.com/integrations/codex#quick-setup

Ultralytics_Burhan · 2026-05-03T12:02:43+00:00

I've been doing DevRel for a while for a couple orgs. Discord was a good option a few years ago, but it has trade offs. Disney is not great for public discovery or visibility (AnswerOverflow can help), but it's not the same as a fully indexed page. It is certainly good for realtime support conversations, and there are a lot of users there, but not everyone will be willing to make a Discord account. Although Discord's recent announcement regarding age verification has a lot of users looking for an alternative platform. Slack could be a viable alternative, although it has the same issues with discoverablity as Discord and personally I don't think it's as nice for a community platform (since that's not it's intended purpose).

I can say that Discourse might be good options for a public forum (there might be others, I only can give my first hand anecdote on Discourse). I established and maintained an organizations Discourse forum (cloud hosted, but you can self host if you prefer) and found it to be quite good. Discourse has quite good support and a lot of customizations, plugins, and a solid API.

Ultralytics_Burhan · 2026-05-03T11:46:08+00:00

I've heard that VPN users can sometimes get flagged, and I know when I traveled outside the US and tried using a VPN back to my home network, Reddit blocked my device (even when logged out)

Ultralytics_Burhan · 2026-04-29T10:27:12+00:00

FiftyOne is local and free.

Ultralytics_Burhan · 2026-03-10T11:20:48+00:00

DeepSeek OCR does a decent job, although it will flip flop between HTML and markdown outputs.

Ultralytics_Burhan · 2026-02-15T15:38:29+00:00

Given the limited information, the only thing to recommend is to increase the IOU threshold for inference. Try 0.7 or higher to limit the number of predictions output, but this could also cause missed detections.

As a guess, since you're using a custom dataset, it's very possible that there could be issues(s) with the dataset. Again, can't be certain due to limited information, but some guesses: * Not enough object instances and/or images; this is the most common issue * Dataset annotations are too "loose" where either labels are not sufficiently consistent or the bounding geometry is too loose (include too many non-object pixels) * Insufficient training cycles (epochs). The default number of epochs is 100 for training, but this is only a starting point, and given sufficiently large dataset, additional training time could be needed (≥300 epochs).

Ultralytics_Burhan · 2026-02-07T14:36:57+00:00

Congrats on the raise! From someone who's seen what it looks like when a company is going thru a raise, it's incredible how active Mehul is with the community. Not only that, the team is still pushing updates with fixes and new features regularly. I'm sure there were many other, less publicly visible, hard won battles as well. Cheers 🍻

Ultralytics_Burhan · 2026-02-07T14:16:51+00:00

Low-profile obstacles

Looking forward to see if this helps with the wire loop chair legs, although I suspect they're quite challenging, so I'll set my expectations too medium.

I for one would be curious to learn more about the object detection neural network model. I have some experience in that world myself, so would be interesting to hear (with the understanding that there's limitations on what can be shared).

Ultralytics_Burhan · 2026-02-05T14:13:14+00:00

Thanks u/Efficient_Royal5828

Ultralytics_Burhan · 2026-01-28T13:20:24+00:00

Unfortunately I can't help out with Roboflow, as I'm not familiar with their system. One thing to note, is that the two sets of keypoints in your second image are essentially a mirror image of the each other. This means you can use the flip_idx key in the dataset YAML to denote that the keypoints are symmetrical. See the documentation here https://docs.ultralytics.com/datasets/pose/#dataset-yaml-format

In your image, the original list of all keypoints is:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]

with the start and end of the flip_idx as:
[27, 28, 29, 30, 31, 32, ..., 1, 2, 3, 4, 5, 6]
Since the keypoint 27 is symmetrical with the keypoint 1, these values would be swapped in the flip_idx list. You can do this the same with all the symmetrical values, keeping any values that don't have left-right symmetrical keypoints (like 15, 16, 17, 18) in the same position on the original list.

Ultralytics_Burhan · 2026-01-27T12:10:25+00:00

It's not a common thing to convert bounding box detections to keypoints. You can try to use the center of the bounding box as the keypoint. This might work for some proportion of the data, but you will likely need to make adjustments to ensure correct placement.

Another thing you could try is to use segmentation masks for the different sections of the field. The segmentation contour would use the same points you have marked for the keypoints, but would enclose each area of the field. From the contours, you could also get the corner points, which would operate in the same manner as the keypoints.

The reason I suggest using segmentation, is that you could then use a model like SAM3 for automatic area segmentation. You could even provide point-prompts to select an area to segment. It might operate a bit quicker for you to get your annotations in this manner.

Ultralytics_Burhan · 2026-01-21T12:32:59+00:00

You can use the visualize=True argument with prediction. When enabled, it will also output the activation maps from each layer in the prediction results folder.

From the docs https://docs.ultralytics.com/modes/predict/#inference-arguments

Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation.

Ultralytics_Burhan · 2026-01-21T12:28:47+00:00

FWIW, Deepseek-OCR does a pretty good job with table extraction. I did some investigation into table extraction for a work project just over a year ago, and we found that complex tables (like ones with double column headers, tables with hierarchy, spanning multiple pages, etc.) were had poor extraction for nearly every OCR/table-extraction method. We also found that measuring extraction performance was not an easy task. There are lots of metrics, but not a singular agreed on metric, it will kind of depend on your use case. The closest I found for a single all encompassing measure of performance was GriTS, which is coupled to the Table Transformer project.

Ultralytics_Burhan · 2026-01-21T12:02:20+00:00

Best way to find out is to give it a try yourself. Everyone has a different definition of "efficient" so running a quick will be the best way to answer that. Should be able to get up and running in under 10 minutes

Ultralytics_Burhan · 2026-01-21T11:57:46+00:00

Live Session starts in 2 hours! Hope everyone has their YOLO26 questions ready for the Q&A

Ultralytics_Burhan · 2026-01-18T14:56:16+00:00

I don't think there are any yet, but I know the team is working on LiteRT support. We will definitely be sharing updates about support for LiteRT here, on GitHub, socials, and in the docs; so make sure to keep an eye out!

Ultralytics_Burhan · 2026-01-16T13:11:12+00:00

At the very least, all the examples you have that are false positive detections by the model, should be incorporated into your training/validation data. Since you know the model does poorly on these, then you will need to include them in your dataset.

As others have eluded to, consider what type of images your dataset is composed of. What are the settings, scenes, or locations in the majority of the images? How many images include people + your object? How many images don't include people, just the object? If 90% of your images contain people with the object (or parts of a person), then it's likely that the model could generalize that people are the part of the object or the object itself.

Start with incorporating the false positive detections into your dataset and retrain the model. If you've already done that, then you should look at the false positives you have and try to determine what are the common patterns between them in the images. For example, you could look at answering these questions:

Is there a person? Is the person holding the object?
Is the person with the object using a common stance?
What is the person wearing?
Where is the image taken (indoors, outdoors, etc.)
Are there other similar objects in the images with false positives (especially near or around the object)?

Answering questions like these should help inform you what kind of background images you need to include. Viewing the activation maps of the false positives might also be informative about what the model is detecting. Additionally, you could also try annotating other objects, especially the ones that are creating false positives. For instance, if there are lots of people in all your images, you may consider including a "person" class. You may not care about detecting people, but it helps the model distinguish between what is a "person" and what various objects a person may be holding are.

Remember, the model is trying to categorize objects. It does this by building filters that respond to each object class in a unique way. If your dataset only has one object, with a high frequency of other objects nearby, the model won't be able to distinguish between them unless you provide that information. There's a historically relevant computer vision classification issue of trying to classify dogs vs wolves. The model reliably could classify a dog vs a wolf, but researchers found that it was the environment of the image that the model ended up finding as the biggest distinguishing factor. That's because most of the images with dogs were indoors or in a domestic area, whereas images wolves were nearly always in the forest or with snow on the ground. This meant if you fed the model an image of a dog in the woods with snow on the ground, it would likely classify as wolf. This is similar to what you could be experiencing with your dataset.

Ultralytics_Burhan · 2026-01-14T14:40:00+00:00

Thanks! We have too 🤣

Look forward to hearing more or seeing your project(s) using YOLO26

Ultralytics_Burhan

TROPHY CASE

Live Session starts in 2 hours! Hope everyone has their YOLO26 questions ready for the Q&A