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This is really scary. I discovered this because we're working on converting and re-hosting popular datasets in many popular formats for easy use across models..
by yeldarb 7y ago
This is really scary. I discovered this because we're working on converting and re-hosting popular datasets in many popular formats for easy use across models... I first noticed that there were a bunch of completely unlabeled images.
Upon digging in, I was appalled that fully 1/3 of the images contained errors or omissions! Some are small (eg a part of a car on the edge of the frame or a ways in the distance not being labeled) but some are egregious (like the woman in the crosswalk with a baby stroller).
I think this really calls out the importance of rigorously inspecting any data you plan to use with your models. Garbage in, garbage out... and self-driving cars should be treated seriously.
I went ahead and corrected by hand the missing bounding boxes and fixed a bunch of other errors like phantom annotations and duplicated boxes. There are still quite a few duplicate boxes (especially around traffic lights) that would have been tedious to fix manually, but if there's enough demand I'll go back and clean those as well.
- kleer001 7y agoI understand your concern and share it myself. This is an important time and we should be really careful training these things. However, training as used in the real world isn't on a still frame only basis, it's used in sequence. And while a single frame might be missing a label, I bet that at-speed most everything important gets labeled correctly enough to be better than a distracted human driver, or the average human driver for that matter. Then, of course, my intuition might be incorrect.
- rhizome 7y ago>And while a single frame might be missing a label, I bet that at-speed most everything important gets labeled correctly enough to be better than a distracted human driver, or the average human driver for that matter. Would you bet a family member? That a distracted driver is a hazard does not mean other drivers are safe, or even safer.
- pathseeker 7y ago>Would you bet a family member? "think of the children!" Lives at stake don't change anything here. The question is whether self-driving cars, even with the errors, are safer for people than regular drivers on average. If so, then absolutely yes everyone should bet their lives and their families'. Thousands of people are dying every day in cars. This is not something we need to wait for it to be perfect. It only needs to be better.
- rifung 7y ago> Lives at stake don't change anything here. The question is whether self-driving cars, even with the errors, are safer for people than regular drivers on average. If so, then absolutely yes everyone should bet their lives and their families'. Maybe logically that makes sense but from an ethical perspective I argue it's much more complicated than that (e.g. the trolley problem) In the current system if a human is at fault, they take the blame for the accident. If we decide to move to self driving cars that we know are far from perfect but statistically better than humans, who do we blame when an accident inevitably happens? Do we blame the manufacturer even though their system is operating within the limits they've advertised? Or do we just say well, it's better than it used to be and it's no one's fault? When the systems become significantly better than humans, I can see this perhaps being a reasonable argument, but if it's just slightly better, I'm not sure people will be convinced.
- craftinator 7y agoI'm voting for the "less dead people" option. Mostly because I'm a selfish person, been in automobile accidents caused by lapsing human attention, and I want it to be less likely that I'll die in a car crash.
- cameronbrown 7y agoBut it's not just about quantity. It's also _different_ people who will die. That radically alters things from an ethical perspective.
- natalyarostova 7y agoYou currently do every day when you let your family members drive with senior citizens.
- kleer001 7y ago> Would you bet a family member? I bet myself and my passengers and everyone else on the road every time I take the wheel. That's what seat belts and other safety measures are for. I'm also willing to take those risks of other drivers I can't control. Why wouldn't I extend those risks one more step to a proven system? (The onus is on proof of that risk)
- klyrs 7y agoAt what framerate? With 30 fps, a 60mph vehicle advances about 5 feet per frame. Every incorrectly labeled frame delays the reaction by a nontrivial amount. My intuition is that your trained accuracy will not exceed the accuracy of the training set. This is literally a matter of life and death; every frame matters.
- broehrl 7y agoTesla claims using 110 fps in their older and 2,300 fps in their newer cars. (https://en.wikipedia.org/wiki/Tesla_Autopilot#Hardware_3 https://en.wikipedia.org/wiki/Tesla_Autopilot#Hardware_3)
- jakecopp 7y agoFollowing the wikipedia source: > CEO Elon Musk says that it is capable of processing 200 frames per second and Tesla’s hardware 3 computer, which is optimized to run a neural net, will be able to handle 2,000 frames per second with redundancy. No way they're shooting 2,000fps, let alone making that many adjustments per second. Maybe that's just a radar ping/signal frequency? I like Elon, but why does he keep saying things like this?
- Florin_Andrei 7y ago> With 30 fps What is the framerate actually used by real-world systems, such as Tesla cars?
- barkingcat 7y agoYour intuition should be that almost everything is incorrectly labelled. And even more so on a large scale sequential basis. If you look at public datasets, it's more often that things are incorrectly tagged/labeled rather than correctly. Entropy is a real thing.
- tomnipotent 7y ago> This is really scary. No, it's not even remotely "really scary". No one is putting an actual self-driving car on the market using this specific data set. Disingenuous to pretend this is any indication of the data using by serious companies in the space or is represented of the impact a few mislabelled samples have on the ability of these systems & algorithms to generalize.
- mumblemumble 7y agoIt's scary when you consider two other factors: First, the AI hype train. People think that calling something "Artificial Intelligence" implies that it is artificial, yes, but also, critically, that it is intelligent. Many enthusiastic people, and also many policymakers, don't fully realize the extent to which machine learning is constrained by both the quality and nature of its training data, and the capabilities of the larger, non-intelligent, software framework that it's being plugged into. Second, that we have one case study - the post-mortem analysis of the fatal pedestrian collision in Arizona - that strongly indicates that commercial products are not free of the sorts problems being highlighted here, and that, unlike what others have suggested, misclassification problems aren't necessarily an issue that's isolated to individual frames and that will come out in the wash when the software is dealing with a stream of frames. Me, I think that self-driving cars are probably a lot like nuclear power. In theory, yes, it is a great idea. In practice, there are a lot of little details that one must get right, and there seem to be a whole lot of opportunities for flaky engineering decisions and incompetent public policy, both enabled by insufficiently-tempered optimism, to scuttle the whole thing.
- tomnipotent 7y agoI'm not sure what point you're trying to make, but it has nothing to do with some mislabeled examples. Every system using supervised training assets has labeling issues, and the world hasn't ended yet. Take a look at Google Translate. This is only "scary" if you're ignorant of the problem. The OP is selling something, and it's in their best interest to spread FUD to sell it.
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- jacquesm 7y agoYou would hope that the software would be resilient enough that missing or even faulty labels would not be the cause of problems. It is by the way relatively easy to - once you reach some arbitrary threshold of precision - to detect such missing or wrong labels automatically, a simple suggestion UI with an accept / reject button could take care of supplying the bulk of the missing labels and correcting the bulk of the errors.
- SolaceQuantum 7y agoHow would one reach a threshold of precision if the data on which precision is determined is missing or faulty?
- jasonwatkinspdx 7y agoThe simple idea is to do stuff like train your model on randomized subsets of your data and then compare its performance to using all the data you have. This doesn't overcome a true "black swan", but that's not what NN are meant to be doing anyhow.
- SolaceQuantum 7y ago"The simple idea is to do stuff like train your model on randomized subsets of your data and then compare its performance to using all the data you have." How do you do this when you cannot verify that your data, in subset or in whole, is accurate? And furthermore you don't know how inaccurate it is?
- TomVDB 7y agoMy intuitive take on it: You train on a subset of the initial data. Even if the data has a certain number of incorrect frames, it should still do a decent job getting a lot of things right. Then you manually loop through all the images of the data set for which the network has detected something that isn't present in the annotations (and vice versa). If the network correctly identified a missing item that wasn't in the original set, all you need to do is press "correct" (and, again, vice versa). You now have an improved data set. Retrain, rinse, repeat. Eventually, you'll converge to a case where you have consistency between training and annotations. And then, you manually go through all images again to weed out the final mistakes. The benefit of this method is that it's much faster to click "correct" that it is to draw rectangles on the screen to label something.
- jxramos 7y agoDoes this dataset have an auxiliary field for each data point to note if it's been human reviewed? We used to train models with only those datapoints that had n=3 concordance in manual reviewers.
- yeldarb 7y agoIt doesn't, but since the original was supposedly human generated and I've now checked them all (twice) it should be n=2. Would love for someone to triple check! If you find errors let me know and I'll be happy to correct them.
- jasonwatkinspdx 7y agoNo it's not scary in the slightest. Missing or scrambled labels are routine in ML and the algorithms are able to handle it. This was a huge paper a couple years ago that demonstrates deep nets will still find the structure in the data even with totally randomized labels: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1611.03530 There are also various well known techniques to verify whether you've over or underfit.
- bo1024 7y agoStrongly disagree. ML is traditionally deployed in low-risk scenarios like image labeling or recommendations where 1% or 5% error rate is totally "routine" and acceptable. Doesn't mean this kind of ML pipeline can translate to cases where errors are literally fatal.
- sukilot 7y ago> deep nets will still find the structure in the data Yes, but in the training data, not in the non-training data! We want the cars to avoid real pedestrians, not only the ones labelled in the training set! From the paper: > When trained on a completely random labeling of the true data, neural networks achieve 0 training error. The test error, of course, is no better than random chance as there is no correlation between the training labels and the test labels.
- mycall 7y agoI would think you want some images that cut off vehicles since this is how cameras will work at times, no?
- notatoad 7y agoI think the problem is not that the car is cut off, its that the cut-off portion of the car isn't labeled. If that's your training data, then you're training the system to interpret cut-off bits of cars as not cars.