7 ms·

This is current state of the art, not sure if its compared or not https://github.com/openalpr/openalpr https://github.com/openalpr/openalpr

lqdc13 10y ago
Possibly unrelated: Anyone knows if confidence intervals can be estimated on probabilities for neural nets?
- tgflynn 10y ago
  Typically you can treat the output value as a probability. This is supposed to be the case with a softmax output layer (as is currently recommended for multiclass classifiers) and I believe it should also work with multiple logistic outputs if you normalize the output vector to sum to 1. In my experience sometimes both the decision and the confidence will be wildly wrong (ie. wrong answer with 99% probability) but most of the time this works reasonably well.
  - woodman 10y ago
    > ...decision and the confidence will be wildly wrong... Isn't "confidence" measuring the internal state of the net, the probability based on classes known to the net? In that case the answer can be wrong, but "confidence" is never wrong.
    - tgflynn 10y ago
      Isn't "confidence" measuring the internal state of the net I don't quite follow your meaning but no, that's not quite how I think about it. If x is the input image and C is the class selected by the classifier we expect the confidence to be representative of the conditional probability P( C | x ). There is theory that shows this to be true given certain assumptions, but as is very often the case in this field the extent to which those assumptions are valid is rarely clear. So if the net outputs a confidence of 0.9999 we expect there to be only 1 chance in 10000 that the true class is something other than C. If we test the net and find such a confidence associated with an incorrect classification more frequently than predicted then we can say that the confidence is objectively incorrect.
      - woodman 10y ago
        It sounds like you are describing accuracy, whereas confidence is closer to a measure of precision. You can have class outputs with low confidence (relative to 1) but still have a highly accurate NN - so long as the correct output has a higher confidence relative to the other outputs (even if the difference is 0.0001). If you've got 4 outputs and you're getting results like {0,1,0,0}, then you should really make sure that you aren't overfit - because that is the most obvious sign of overfitting.
        tgflynn 10y ago
        I think you're describing accuracy, as in prediction accuracy. Confidence is simply how much confidence you have that the predicted class is correct. This is often important because in a real system you often want to know whether to accept the model's prediction or pass the case on to a more accurate, more expensive model (sometimes a human). You then can select a confidence threshold that gives an acceptable trade-off between false positives and false negatives (ie. choose an operating point on the ROC curve). If your model just gives a yes/no answer with no measure of confidence, or if you don't know how to interpret the output as a confidence, then you don't have sufficient information to do this. Over-fitting is often part of the problem but in my experience things aren't as black or white as you seem to imply. There are things you can do to limit over-fitting but I don't know of any simple test that will give you a 0 if you're over-fitting and 1 if you're not.
        woodman 10y ago
        > Confidence is simply how much confidence you have that the predicted class is correct. If you mean correct among the number of output nodes, then we are talking about the same thing. If you mean correct among all the possibilities to include those unknown to the NN, then I'd appreciate a link - because I've never seen that before and it sounds incredibly useful. > ...I don't know of any simple test that will give you a 0 if you're over-fitting and 1 if you're not. Neither do I, that is why I used all those weasel words - but I also don't know of a neural network that wasn't either being misused or being overfit that returned such results. A simple example would be the 2 inputs for the xor test data, a hidden layer with enough nodes to memorize every permutation, and 2 output nodes - each representing the confidence for the potential result of the binary operation.
  - argonaut 10y ago
    Those aren't confidence intervals.
    - tgflynn 10y ago
      You may be correct technically. I'm not familiar with the formal definition of "confidence intervals" in statistics. Informally we often speak of confidence as an estimate of the conditional probability P( C | x ) where C is the class returned by a classifier and x is the input. That's what I thought the parent comment was referring to but I may have been mistaken. EDIT: For my own information what would knowing the confidence intervals tell you that P( C | x ) wouldn't ?
      - versteegen 10y ago
        If C is binary or categorical, then confidence intervals don't make sense to talk about (although you could still talk about them if C is integer valued), and the equivalent thing is P(C|x). Unless I guess if you have a posterior distribution over distribution... The definition of a Q (e.g Q = 0.95) confidence interval a < X < b is just that P(a < X < b) = Q. It's not even necessary that the interval contain the most probable value of X, but when you actually calculate them to want to tack on extra constraints to prevent stuff like that, like minimising the length of the interval.
        lqdc13 10y ago
        If C is binary or categorical, it totally makes sense to talk about confidence intervals. Why wouldn't it? You are trying to figure out if your probability output for a class is reliable for that sample.
        argonaut 10y ago
        Ah, you're right.
ratsbane 10y ago
The openalpr project is pretty good, but it uses an older approach with lot of hand-coded logic instead of the convolutional neural net approach of this project. openalpr is faster than this, at least now, but I think the neural net has potential to be faster and more accurate.
- argonaut 10y ago
  Hard to tell. Unfortunately neither this nor the openalpr project have any benchmarks or evaluations on a test set. It could be that the openalpr project already gives you 99% accuracy (unlikely), in which case there really isn't a point in trying to optimize it more (e.g. move on to harder problems).
- visarga 10y ago
  The author could take a camera on the road and shoot pics for a day or two, then use OpenALPR to predict the plates and check this dataset to see how accurate it is. If it's good enough, it can be used to train a CNN model with real images of cars and no manual labeling.
  - ratsbane 10y ago
    I started saving file from my dashcam several months ago with that idea - to use the openalpr results as training data for a neural net, but the results I'm getting from openalpr aren't good enough to use without a lot of manual review.
Mizza 10y ago
Cool, thanks for the reference point. Interesting that it's an AGPL project!
- zo1 10y ago
  The only problem with that project is that the authors "reuse" the "OpenALPR" exact name for a commercial, paid, hosted SaaS version of it. It's devious sneak-marketing that piggy-backs off of the open-source portion's good will.
roel_v 10y ago
If this is the state of the art, then we needn't be very worried. The demo site shows horrible results - the same plate is recognized 2 dozen times as different numbers every time, over a time span of 2 seconds. I'm pretty sure there are solutions available that works much better though, my point is that describing openalpr as 'state of the art' isn't quite accurate.
- abrookewood 10y ago
  I think you are misreading it. It is listing the top 10 possibilities that might represent the number plate and the expected accuracy of each one, with the top suggestion being correct.
  - roel_v 10y ago
    Yes I got that part, but it still turns out I'm the idiot here - I had set the view mode to 'individual plates' instead of 'plate groups' (I didn't understand what it was for so I was poking around) and then it shows you the separate images of each car, instead of grouping by car and taking the most likely from several attempts, which was was I complained they didn't do in the GP. When I look at the right data, they get all but one of the two dozen or so I checked right. Impressive (and a bit scary).

lqdc13 10y ago

Possibly unrelated: Anyone knows if confidence intervals can be estimated on probabilities for neural nets?

tgflynn 10y ago
Typically you can treat the output value as a probability. This is supposed to be the case with a softmax output layer (as is currently recommended for multiclass classifiers) and I believe it should also work with multiple logistic outputs if you normalize the output vector to sum to 1. In my experience sometimes both the decision and the confidence will be wildly wrong (ie. wrong answer with 99% probability) but most of the time this works reasonably well.
- woodman 10y ago
  > ...decision and the confidence will be wildly wrong... Isn't "confidence" measuring the internal state of the net, the probability based on classes known to the net? In that case the answer can be wrong, but "confidence" is never wrong.
  - tgflynn 10y ago
    Isn't "confidence" measuring the internal state of the net I don't quite follow your meaning but no, that's not quite how I think about it. If x is the input image and C is the class selected by the classifier we expect the confidence to be representative of the conditional probability P( C | x ). There is theory that shows this to be true given certain assumptions, but as is very often the case in this field the extent to which those assumptions are valid is rarely clear. So if the net outputs a confidence of 0.9999 we expect there to be only 1 chance in 10000 that the true class is something other than C. If we test the net and find such a confidence associated with an incorrect classification more frequently than predicted then we can say that the confidence is objectively incorrect.
    - woodman 10y ago
      It sounds like you are describing accuracy, whereas confidence is closer to a measure of precision. You can have class outputs with low confidence (relative to 1) but still have a highly accurate NN - so long as the correct output has a higher confidence relative to the other outputs (even if the difference is 0.0001). If you've got 4 outputs and you're getting results like {0,1,0,0}, then you should really make sure that you aren't overfit - because that is the most obvious sign of overfitting.
      - tgflynn 10y ago
        I think you're describing accuracy, as in prediction accuracy. Confidence is simply how much confidence you have that the predicted class is correct. This is often important because in a real system you often want to know whether to accept the model's prediction or pass the case on to a more accurate, more expensive model (sometimes a human). You then can select a confidence threshold that gives an acceptable trade-off between false positives and false negatives (ie. choose an operating point on the ROC curve). If your model just gives a yes/no answer with no measure of confidence, or if you don't know how to interpret the output as a confidence, then you don't have sufficient information to do this. Over-fitting is often part of the problem but in my experience things aren't as black or white as you seem to imply. There are things you can do to limit over-fitting but I don't know of any simple test that will give you a 0 if you're over-fitting and 1 if you're not.
        woodman 10y ago
        > Confidence is simply how much confidence you have that the predicted class is correct. If you mean correct among the number of output nodes, then we are talking about the same thing. If you mean correct among all the possibilities to include those unknown to the NN, then I'd appreciate a link - because I've never seen that before and it sounds incredibly useful. > ...I don't know of any simple test that will give you a 0 if you're over-fitting and 1 if you're not. Neither do I, that is why I used all those weasel words - but I also don't know of a neural network that wasn't either being misused or being overfit that returned such results. A simple example would be the 2 inputs for the xor test data, a hidden layer with enough nodes to memorize every permutation, and 2 output nodes - each representing the confidence for the potential result of the binary operation.
- argonaut 10y ago
  Those aren't confidence intervals.
  - tgflynn 10y ago
    You may be correct technically. I'm not familiar with the formal definition of "confidence intervals" in statistics. Informally we often speak of confidence as an estimate of the conditional probability P( C | x ) where C is the class returned by a classifier and x is the input. That's what I thought the parent comment was referring to but I may have been mistaken. EDIT: For my own information what would knowing the confidence intervals tell you that P( C | x ) wouldn't ?
    - versteegen 10y ago
      If C is binary or categorical, then confidence intervals don't make sense to talk about (although you could still talk about them if C is integer valued), and the equivalent thing is P(C|x). Unless I guess if you have a posterior distribution over distribution... The definition of a Q (e.g Q = 0.95) confidence interval a < X < b is just that P(a < X < b) = Q. It's not even necessary that the interval contain the most probable value of X, but when you actually calculate them to want to tack on extra constraints to prevent stuff like that, like minimising the length of the interval.
      - lqdc13 10y ago
        If C is binary or categorical, it totally makes sense to talk about confidence intervals. Why wouldn't it? You are trying to figure out if your probability output for a class is reliable for that sample.
      - argonaut 10y ago
        Ah, you're right.

ratsbane 10y ago

The openalpr project is pretty good, but it uses an older approach with lot of hand-coded logic instead of the convolutional neural net approach of this project. openalpr is faster than this, at least now, but I think the neural net has potential to be faster and more accurate.

argonaut 10y ago
Hard to tell. Unfortunately neither this nor the openalpr project have any benchmarks or evaluations on a test set. It could be that the openalpr project already gives you 99% accuracy (unlikely), in which case there really isn't a point in trying to optimize it more (e.g. move on to harder problems).
visarga 10y ago
The author could take a camera on the road and shoot pics for a day or two, then use OpenALPR to predict the plates and check this dataset to see how accurate it is. If it's good enough, it can be used to train a CNN model with real images of cars and no manual labeling.
- ratsbane 10y ago
  I started saving file from my dashcam several months ago with that idea - to use the openalpr results as training data for a neural net, but the results I'm getting from openalpr aren't good enough to use without a lot of manual review.

Mizza 10y ago

Cool, thanks for the reference point. Interesting that it's an AGPL project!

zo1 10y ago
The only problem with that project is that the authors "reuse" the "OpenALPR" exact name for a commercial, paid, hosted SaaS version of it. It's devious sneak-marketing that piggy-backs off of the open-source portion's good will.

roel_v 10y ago

If this is the state of the art, then we needn't be very worried. The demo site shows horrible results - the same plate is recognized 2 dozen times as different numbers every time, over a time span of 2 seconds. I'm pretty sure there are solutions available that works much better though, my point is that describing openalpr as 'state of the art' isn't quite accurate.

abrookewood 10y ago
I think you are misreading it. It is listing the top 10 possibilities that might represent the number plate and the expected accuracy of each one, with the top suggestion being correct.
- roel_v 10y ago
  Yes I got that part, but it still turns out I'm the idiot here - I had set the view mode to 'individual plates' instead of 'plate groups' (I didn't understand what it was for so I was poking around) and then it shows you the separate images of each car, instead of grouping by car and taking the most likely from several attempts, which was was I complained they didn't do in the GP. When I look at the right data, they get all but one of the two dozen or so I checked right. Impressive (and a bit scary).