Frigate+ 2025.3 Base Model Update #20552
Replies: 27 comments 78 replies
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I've been using sheep for over a year now. This is apparently a sheep. |
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Thank you for adding those newer candidate labels! Finally have a label for my otter family (mustelid) and giving family warning of coyotes. I have a sky cam, live near ATL airport approach and a lake so hoping to add some annotations for airplane / drone. Im seeing some significant improvements in false positives already this past evening. BTW: A badger is technically a mustelid |
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Big thanks to the whole Frigate crew - devs, contributors, testers etc and the whole community. So awesome to see the new base image drop with AUSPOST labels. I’ve been wanting to add a label the postie for quite some time now, so I’m stoked to finally start adding some Aussie-specific data into the mix. Cheers from Australia 🇦🇺 |
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My wife and small dogs thank you for improving false positives on deer. They were tired of being hit with the sprinkler automation I use to keep the deer away from my landscaping. |
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Maybe it's an "feature request", but are you considering that these new base models will be available with a one time payment instead of a year Frigate+ subscription? I would love to test these models, but have no intention of using other frigate+ functions, since I just don't like to share images online :) Currently I am using Yolov9 model s with 640*640 and really wondering how much difference this trained model is. |
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Great job Frigate team. Excited to see what the future brings, a la user built classification flows and the triggers. |
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Maybe one for somewhere else, The new 2025.3 Base Model doesn't appear in my Frigate+ account. Does this take a while to propagate down and become available to all accounts? Thanks- Looking forward to trying it out, each revision so far has made massive leaps forwards - really impressed! WD |
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Regarding Frigate+ models - When should a detection be submitted to Frigate+ ? For example, if it is having no issues detecting cars, should car detections no longer be submitted? Or if it typically has no issues, but If get a false car detection, should that false one be submitted and no others? And if I really want to get good detections for people seen in my driveway, should I keep submitting detections even when it seems to be doing a good job? |
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I have a model that I trained about a month ago so I need to update, but I'm never sure what objects my currently loaded model supports. I've tried adding other animals in the past with prior models that were listed on frigate+ as being supported only to get an error on startup. What is the best way to enumerate what objects your current model supports so you don't have to play the guessing game in the config? Similar question the the previous - I've been having relatively poor luck with fox and raccoon and it seems to have gotten worse with the last two F+ models I've trained - both within the past 6 months. I've got very good exemplar images of all of the objects I really care about, do I need to submit additional images when I request my latest model or will it retrain the latest base model with all of my existing images? Ideally I'd like to train the new base model on the currently existing images, and from what's been said my results should be better. Great news about the new model, really looking forward to trying this out - please keep it up! |
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I just requested a model 2 days ago. Is there a way to get it retrained on .3 aside from requesting a new model again? |
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Im curious, every time a new base model releases i read how it improves new labels. I might speak only for myself but human and car detection are my biggest use cases, have those been not improved in the last year releases? Has the peak of performance been reached for these? |
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Could we get "Police" added as a label? Would be kinda interesting to know when they're in the neighborhood....just because I'm nosey, not because I've got anything to be concerned about, of course |
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hi there folks, best regards sry for my bad english, its´s school learned at 1981 ^^ |
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I have about 6k submitted images, many of them with dogs (most being of my own dog). I do have a a handful of submitted images with actual coyotes. They were detected as "dog", and I submitted to Frigate+ with "Yes this is a dog" and verified as such - which is true if the word dog is used generically to mean canine. I suspect I'm not the only one to do this. The fundamental question is, should I go back and relabel these coyotes as such? More generally, I'm curious about any model's ability to consistently distinguish coyotes and pet dogs. My own dog still sometimes gets mislabeled as a cat. I can imagine some pet breeds like German Shepard or Malamute getting loose, and, depending on picture quality and contextual hints, a lot of humans would have trouble distinguishing them from a coyote (or wolf or jackal). A more general question - as the number of images submitted to Frigate+ grows, when we opt-in for a new label, re-verifying images becomes increasingly tedious. For example, I recently switched from mobiledet to yolov9, and I got my first skunk detection. That was with about 1500 images submitted for that camera, so it was pretty tedious to go through all those and re-verify all of them for the single skunk. On the other hand, the second time through I did catch some stuff I originally missed. (Please don't take this as a complaint - just looking for a discussion on how to tackle this problem. In other words, I'd like to go back and fix the coyote labels, but it's a lot of time to sort through all the images; and, personally speaking, I'm content to have all canines detected as dog.) (Edit: fix typo) |
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Is there a bug in the GUI?
What does CURRENT represent? |
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How recent is recent RE: model upgrades for recent requests? I submitted a request on 17/10 - but it seems to have stayed on 2025.2. |
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how are you guys submitting false positives? |
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I am running the mobiledet model on two TPUs with 10 4k cameras. I am considering upgrading to a hailo8 and yolo9 models. If I understand correctly, new base models include the fine tuning images previously submitted. Will the YOLO9 model include my previous images? Since I have to specify the model when I submit images for training, I suspect they are not included in the YOLO9 models and I'll need to start over. You guys with experience running Hailo8 and YOLO9, do you recommend the change from Coral TPUs and the mobiledet model? |
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for new candidate labels, I should not add them in frigate config, correct? as they are not in actual Frigate yet? |
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has anyone else experienced a relatively drastic increase of false positives for 'person' on 2025.3? i'm currently getting 10-20 alerts for a person daily for each of my cameras vs pretty much zero before the new model (other than real positives). fwiw this is the first time i have not tried to use the newly released base model but instead processed some more of my image backlog before requesting a fine-tuned model. this added about ~450, totalling at 2.2k now - most of which was adding real positives for birds, foxes, squirrels and cats for which i've had lots of false positives before and so trying to balance things out. my previous attempts at using the base model all resulted in lots of false positives - might be worth noting that this is almost always "extreme" false positives, not objects of label 'person' being misidentified as 'dog' (those did happen to me too but they are very rare) but just random bits of foliage, branches, lens flares, potted plants etc. this is the first time i've had this much false positives on a fine tuned model (possibly for other labels too - i mostly notice 'person' because for these i get home assistant notifications). i'm still on mobiledet/edgetpu because yolo on my gpu results in 10x+ more energy consumption (~2.5W for the coral vs additional ~30W for the gpu when using yolov9t onnx 320) while inference is twice slower (~8ms for the coral, ~16ms for the gpu) 😅 detection is at 5fps, 1280x720, motion masks in place as much as practical. |
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Should we consider adding "mobile phone" as a candidate label? Sometimes I often forget where I've left my phone in the yard, and I believe this label would be very meaningful. |
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I understand the reasoning behind re-verifying images when new labels are added to a camera. However, reviewing thousands of images simply to confirm that a particular object does not appear in them feels excessive. The main issue is that partially verified images (where some labels are confirmed and others are not) are grouped together with completely unverified images. As a result, when I add a new label to a camera that already has over a thousand images, I end up with a large backlog of images requiring review. Then, if I encounter a false positive and submit it, that image gets mixed into the same backlog as those partially verified images, making it difficult to manage and track progress. A separate tab for partially verified pics would probably make life easier for a lot of people. |
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No donkey label included? |
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@zonyl You can probably reduce false positives caused by spiderwebs by setting min/max area of the objects. I also have this buddy hanging around the camera all the time, causing endless motion detection during the night, although I managed to decrease false positives to almost zero. Eventually I will probably move the camera a little bit further away from the roof edge just to save unnecessary processing time.
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When the bounding box of a recognized object is significantly larger than the object itself, or only part of the object is detected (e.g., when the full person is in the frame but only the upper or lower body is recognized), should this be considered a false positive? Also, in cases where the object is occluded or not fully visible in the frame, should it be labeled as "difficult"? |
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FYI this model and the suggestion model on the frigate+ site REALLY strongly prefer to label my cat as a dog now. |
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The base model in Frigate+ has now been updated to 2025.3. All model requests going forward will use this version. If you have a recent model request, it has automatically been updated to use 2025.3.
In this release, sampling was weighted towards labels with fewer examples and you should see significant improvements for bear, deer, fox, kangaroo, motorcycle, rabbit, raccoon, and skunk. Specifically for YOLOv9, I incorporated a more robust sampling of reported false positives into the base model training set.
I have also added many new candidate labels requested over the past few months. These include: rickshaw, wombat, auspost, aramex, bobcat, mustelid, transoflex, airplane, drone, mountain_lion, crocodile, turkey, baby_stroller, monkey, coyote, porcupine, parcelforce, sheep, snake, helicopter, lizard, duck, hermes, cargus, fan_courier, and sameday.
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