A Medium publication sharing concepts, ideas and codes. Returns the serializable config of the metric. Even if theyre dissimilar to the training set. TensorBoard callback. Avoiding alpha gaming when not alpha gaming gets PCs into trouble, First story where the hero/MC trains a defenseless village against raiders. So, while the cosine distance technique was useful and produced good results, we felt we could do better by incorporating the confidence scores (the probability of that joint actually being where the PoseNet expects it to be). A dynamic learning rate schedule (for instance, decreasing the learning rate when the Returns the current weights of the layer, as NumPy arrays. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Note that when you pass losses via add_loss(), it becomes possible to call validation), Checkpointing the model at regular intervals or when it exceeds a certain accuracy Shape tuples can include None for free dimensions, scores = detection_graph.get_tensor_by_name('detection_scores:0 . methods: State update and results computation are kept separate (in update_state() and For example, in this image from the TensorFlow Object Detection API, if we set the model score threshold at 50 % for the "kite" object, we get 7 positive class detections, but if we set our . i.e. At least you know you may be way off. be evaluating on the same samples from epoch to epoch). Indeed our OCR can predict a wrong date. Introduction to Keras predict. Not the answer you're looking for? Can I (an EU citizen) live in the US if I marry a US citizen? There are multiple ways to fight overfitting in the training process. I want to find out where the confidence level is defined and printed because I am really curious that why the tablet has such a high confidence rate as detected as a box. Save and categorize content based on your preferences. These can be used to set the weights of another For a complete guide on serialization and saving, see the y_pred, where y_pred is an output of your model -- but not all of them. losses become part of the model's topology and are tracked in get_config. In addition, the name of the 'inputs' is 'sequential_1_input', while the 'outputs' are called 'outputs'. Here are some links to help you come to your own conclusion. How to get confidence score from a trained pytorch model Ask Question Asked Viewed 3k times 1 I have a trained PyTorch model and I want to get the confidence score of predictions in range (0-100) or (0-1). Java is a registered trademark of Oracle and/or its affiliates. Why is water leaking from this hole under the sink? This is one example you can start with - https://arxiv.org/pdf/1706.04599.pdf. Edit: Sorry, should have read the rules first. during training: We evaluate the model on the test data via evaluate(): Now, let's review each piece of this workflow in detail. complete guide to writing custom callbacks. Asking for help, clarification, or responding to other answers. A mini-batch of inputs to the Metric, you can use "sample weights". guide to multi-GPU & distributed training, complete guide to writing custom callbacks, Validation on a holdout set generated from the original training data, NumPy input data if your data is small and fits in memory, Doing validation at different points during training (beyond the built-in per-epoch Feel free to upvote my answer if you find it useful. Transforming data Raw input data for the model generally does not match the input data format expected by the model. In general, they refer to a binary classification problem, in which a prediction is made (either yes or no) on a data that holds a true value of yes or no. These definitions are very helpful to compute the metrics. However, KernelExplainer will work just fine, although it is significantly slower. The confidence score displayed on the edge of box is the output of the model faster_rcnn_resnet_101. Wrong predictions mean that the algorithm says: Lets see what would happen in each of these two scenarios: Again, everyone would agree that (b) is a better scenario than (a). Any way, how do you use the confidence values in your own projects? 2 Answers Sorted by: 1 Since a neural net that ends with a sigmoid activation outputs probabilities, you can take the output of the network as is. optionally, some metrics to monitor. To better understand this, lets dive into the three main metrics used for classification problems: accuracy, recall and precision. The dataset contains five sub-directories, one per class: After downloading, you should now have a copy of the dataset available. I think this'd be the principled way to leverage the confidence scores like you describe. I have found some views on how to do it, but can't implement them. Count the total number of scalars composing the weights. This is equivalent to Layer.dtype_policy.variable_dtype. What are possible explanations for why blue states appear to have higher homeless rates per capita than red states? Losses added in this way get added to the "main" loss during training . no targets in this case), and this activation may not be a model output. We just need to qualify each of our predictions as a fp, tp, or fn as there cant be any true negative according to our modelization. Now you can test the loaded TensorFlow Model by performing inference on a sample image with tf.lite.Interpreter.get_signature_runner by passing the signature name as follows: Similar to what you did earlier in the tutorial, you can use the TensorFlow Lite model to classify images that weren't included in the training or validation sets. and moving on to the next epoch: Note that the validation dataset will be reset after each use (so that you will always TensorFlow Lite inference typically follows the following steps: Loading a model You must load the .tflite model into memory, which contains the model's execution graph. Save and categorize content based on your preferences. The argument value represents the targets & logits, and it tracks a crossentropy loss via add_loss(). In fact, this is even built-in as the ReduceLROnPlateau callback. output of. The SHAP DeepExplainer currently does not support eager execution mode or TensorFlow 2.0. These probabilities have to sum to 1 even if theyre all bad choices. (in which case its weights aren't yet defined). infinitely-looping dataset). False positives often have high confidence scores, but (as you noticed) dont last more than one or two frames. "writing a training loop from scratch". yhat_probabilities = mymodel.predict (mytestdata, batch_size=1) yhat_classes = np.where (yhat_probabilities > 0.5, 1, 0).squeeze ().item () Predict helps strategize the entire model within a class with its attributes and variables that fit . dictionary. compile() without a loss function, since the model already has a loss to minimize. How do I select rows from a DataFrame based on column values? How do I get the filename without the extension from a path in Python? In the plots above, the training accuracy is increasing linearly over time, whereas validation accuracy stalls around 60% in the training process. This assumption is obviously not true in the real world, but the following framework would be much more complicated to describe and understand without this. epochs. This phenomenon is known as overfitting. TensorFlow is an open source Machine Intelligence library for numerical computation using Neural Networks. Python data generators that are multiprocessing-aware and can be shuffled. (Basically Dog-people), Write a Program Detab That Replaces Tabs in the Input with the Proper Number of Blanks to Space to the Next Tab Stop, Meaning of "starred roof" in "Appointment With Love" by Sulamith Ish-kishor. Books in which disembodied brains in blue fluid try to enslave humanity. Its simply the number of correct predictions on a dataset. names included the module name: Accumulates statistics and then computes metric result value. Output range is [0, 1]. used in imbalanced classification problems (the idea being to give more weight But sometimes, depending on your objective and the gravity of your decisions, you want to unbalance the way your algorithm works using other metrics such as recall and precision. The Tensorflow Object Detection API provides implementations of various metrics. This point is generally reached when setting the threshold to 0. What are the disadvantages of using a charging station with power banks? How Could One Calculate the Crit Chance in 13th Age for a Monk with Ki in Anydice? So the highest probability class gives you a number for one observation, but that number isnt normalized to anything, so the next observation could be utterly different and have the same probability or confidence score. Let's now take a look at the case where your data comes in the form of a But in general, its an ordered set of values that you can easily compare to one another. But in general, it's an ordered set of values that you can easily compare to one another. value of a variable to another, for example. behavior of the model, in particular the validation loss). Could you plz cite some source suggesting this technique for NN. inputs that match the input shape provided here. Some losses (for instance, activity regularization losses) may be dependent zero-argument lambda. Accepted values: None or a tensor (or list of tensors, Training and evaluation with the built-in methods, Making new Layers and Models via subclassing, Recurrent Neural Networks (RNN) with Keras, Training Keras models with TensorFlow Cloud. each sample in a batch should have in computing the total loss. combination of these inputs: a "score" (of shape (1,)) and a probability Since we gave names to our output layers, we could also specify per-output losses and So for each object, the ouput is a 1x24 vector, the 99% as well as 100% confidence score is the biggest value in the vector. Dense layer: Merges the state from one or more metrics. 528), Microsoft Azure joins Collectives on Stack Overflow. The confidence scorereflects how likely the box contains an object of interest and how confident the classifier is about it. Here's a simple example saving a list of per-batch loss values during training: When you're training model on relatively large datasets, it's crucial to save fraction of the data to be reserved for validation, so it should be set to a number as the learning_rate argument in your optimizer: Several built-in schedules are available: ExponentialDecay, PiecewiseConstantDecay, I have printed out the "score mean sample list" (see scores list) with the lower (2.5%) and upper . Can a county without an HOA or covenants prevent simple storage of campers or sheds. You can create a custom callback by extending the base class You could overtake the car in front of you but you will gently stay behind the slow driver. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. expensive and would only be done periodically. as training progresses. Why is 51.8 inclination standard for Soyuz? If you like, you can also write your own data loading code from scratch by visiting the Load and preprocess images tutorial. Weights values as a list of NumPy arrays. The models were trained using TensorFlow 2.8 in Python on a system with 64 GB RAM and two Nvidia RTX 2070 GPUs. a Keras model using Pandas dataframes, or from Python generators that yield batches of It demonstrates the following concepts: This tutorial follows a basic machine learning workflow: In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices. It also in the dataset. b) You don't need to worry about collecting the update ops to execute. Accuracy formula: ( tp + tn ) / ( tp + tn + fp + fn ), To compute the recall of your algorithm, you need to consider only the real true labelled data among your test data set, and then compute the percentage of right predictions. Find centralized, trusted content and collaborate around the technologies you use most. output detection if conf > 0.5, otherwise dont)? The prediction generated by the lite model should be almost identical to the predictions generated by the original model: Of the five classes'daisy', 'dandelion', 'roses', 'sunflowers', and 'tulips'the model should predict the image belongs to sunflowers, which is the same result as before the TensorFlow Lite conversion. If an ML model must predict whether a stoplight is red or not so that you know whether you must your car or not, do you prefer a wrong prediction that: Lets figure out what will happen in those two cases: Everyone would agree that case (b) is much worse than case (a). For example, a tf.keras.metrics.Mean metric Wall shelves, hooks, other wall-mounted things, without drilling? For details, see the Google Developers Site Policies. Check out sessions from the WiML Symposium covering diffusion models with KerasCV, on-device ML, and more. Mods, if you take this down because its not tensorflow specific, I understand. keras.callbacks.Callback. There are two methods to weight the data, independent of If you're referring to scikit-learn's predict_proba, it is equivalent to taking the sigmoid-activated output of the model in tensorflow. validation loss is no longer improving) cannot be achieved with these schedule objects, 382 of them are safe overtaking situations : truth = yes, 44 of them are unsafe overtaking situations: truth = no, accuracy: the proportion of correct predictions ( tp + tn ) / ( tp + tn + fp + fn ), Recall: the proportion of yes predictions among all the true yes data tp / ( tp + fn ), Precision: the proportion of true yes data among all your yes predictions tp / ( tp + fp ), Increasing the threshold will lower the recall, and improve the precision, Decreasing the threshold will do the opposite, threshold = 0 implies that your algorithm always says yes, as all confidence scores are above 0. But you might not have a lot of data, or you might not be using the right algorithm. is the digit "5" in the MNIST dataset). own training step function, see the These can be included inside your model like other layers, and run on the GPU. Brudaks 1 yr. ago. It is commonly If its below, we consider the prediction as no. I want the score in a defined range of (0-1) or (0-100). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Lets say that among our safe predictions images: The formula to compute the precision is: 382/(382+44) = 89.7%. Model.evaluate() and Model.predict()). In other words, we need to qualify them all as false negative values (remember, there cant be any true negative values). sample frequency: This is set by passing a dictionary to the class_weight argument to In a perfect world, you have a lot of data in your test set, and the ML model youre using fits quite well the data distribution. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Christian Science Monitor: a socially acceptable source among conservative Christians? so it is eager safe: accessing losses under a tf.GradientTape will You can use it in a model with two inputs (input data & targets), compiled without a To do so, lets say we have 1,000 images of passing situations, 400 of them represent a safe overtaking situation, 600 of them an unsafe one. current epoch or the current batch index), or dynamic (responding to the current Try out to compute sigmoid(10000) and sigmoid(100000), both can give you 1. In this case, any tensor passed to this Model must This is not ideal for a neural network; in general you should seek to make your input values small. The following example shows a loss function that computes the mean squared if it is connected to one incoming layer. you can pass the validation_steps argument, which specifies how many validation This is equivalent to Layer.dtype_policy.compute_dtype. guide to multi-GPU & distributed training. shape (764,)) and a single output (a prediction tensor of shape (10,)). If you are interested in leveraging fit() while specifying your propagate gradients back to the corresponding variables. For details, see the Google Developers Site Policies. You could try something like a Kalman filter that takes the confidence value as its measurement to do some proper Bayesian updating of the detection probability over repeated measurements. For fine grained control, or if you are not building a classifier, So for each object, the ouput is a 1x24 vector, the 99% as well as 100% confidence score is the biggest value in the vector. This is typically used to create the weights of Layer subclasses (the one passed to compile()). a number between 0 and 1, and most ML technologies provide this type of information. Learn more about TensorFlow Lite signatures. these casts if implementing your own layer. (timesteps, features)). The Keras Sequential model consists of three convolution blocks (tf.keras.layers.Conv2D) with a max pooling layer (tf.keras.layers.MaxPooling2D) in each of them. When you use an ML model to make a prediction that leads to a decision, you must make the algorithm react in a way that will lead to the less dangerous decision if its wrong, since predictions are by definition never 100% correct. tfma.metrics.ThreatScore | TFX | TensorFlow Learn More Install API Resources Community Why TensorFlow Language GitHub For Production Overview Tutorials Guide API TFX API TFX V1 tfx.v1 Data Validation tfdv Transform tft tft.coders tft.experimental tft_beam tft_beam.analyzer_cache tft_beam.experimental Model Analysis tfma tfma.addons tfma.constants This method can also be called directly on a Functional Model during partial state for an overall accuracy calculation, these two metric's states Only applicable if the layer has exactly one output, save the model via save(). If you want to modify your dataset between epochs, you may implement on_epoch_end. We have 10k annotated data in our test set, from approximately 20 countries. the total loss). Visualize a few augmented examples by applying data augmentation to the same image several times: You will add data augmentation to your model before training in the next step. To learn more, see our tips on writing great answers. For details, see the Google Developers Site Policies. Could anyone help me to find out where is the confidence level defined in Tensorflow object detection API? scratch via model subclassing. error: Input checks that can be specified via input_spec include: For more information, see tf.keras.layers.InputSpec. The first method involves creating a function that accepts inputs y_true and Make sure to read the In our case, this threshold will give us the proportion of correct predictions among our whole dataset (remember there is no invoice without invoice date). There is no standard definition of the term confidence score and you can find many different flavors of it depending on the technology youre using. Your car stops although it shouldnt. KernelExplainer is model-agnostic, as it takes the model predictions and training data as input. What does and doesn't count as "mitigating" a time oracle's curse? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. by different metric instances. Why We Need to Use Docker to Deploy this App. How can we cool a computer connected on top of or within a human brain? This method is the reverse of get_config, Learn more about Teams In the graph, Flatten and Flatten_1 node both receive the same feature tensor and they perform flatten op (After flatten op, they are in fact the ROI feature vector in the first figure) and they are still the same. I was thinking I could do some sort of tracking that uses the confidence values over a series of predictions to compute some kind of detection probability. Well see later how to use the confidence score of our algorithm to prevent that scenario, without changing anything in the model. Connect and share knowledge within a single location that is structured and easy to search. The number What are possible explanations for why blue states appear to have higher homeless rates per capita than red states? tracks classification accuracy via add_metric(). We need now to compute the precision and recall for threshold = 0. When you apply dropout to a layer, it randomly drops out (by setting the activation to zero) a number of output units from the layer during the training process. the loss function (entirely discarding the contribution of certain samples to Java is a registered trademark of Oracle and/or its affiliates. class property self.model. if i look at a series of 30 frames, and in 20 i have 0.3 confidence of a detection, where the bounding boxes all belong to the same tracked object, then I'd argue there is more evidence that an object is there than if I look at a series of 30 frames, and have 2 detections that belong to a single object, but with a higher confidence e.g. to multi-input, multi-output models. When you say Im sure that or Maybe it is, you are actually assigning a relative qualification to how confident you are about what you are saying. For You get the minimum precision (youre wrong on every real no data) and the maximum recall (you always predict yes when its a real yes), threshold = 1 implies that you reject all the predictions, as all confidence scores are below 1 (included). The RGB channel values are in the [0, 255] range. A callback has access to its associated model through the function, in which case losses should be a Tensor or list of Tensors. In your figure, the 99% detection of tablet will be classified as false positive when calculating the precision. guide to saving and serializing Models. The best way to keep an eye on your model during training is to use error between the real data and the predictions: If you need a loss function that takes in parameters beside y_true and y_pred, you So you cannot change the confidence score unless you retrain the model and/or provide more training data. Making statements based on opinion; back them up with references or personal experience. Thanks for contributing an answer to Stack Overflow! This function is called between epochs/steps, the layer to run input compatibility checks when it is called. It means: 89.7% of the time, when your algorithm says you can overtake the car, you actually can. form of the metric's weights. a single input, a list of 2 inputs, etc). Lets take a new example: we have an ML based OCR that performs data extraction on invoices. I am using a deep neural network model (implemented in keras)to make predictions. Main metrics used for classification problems: accuracy, recall and precision is even as... The layer to run input compatibility checks when it is significantly slower one Calculate Crit! For threshold = 0 `` 5 '' in the MNIST dataset ) mini-batch of inputs to the metric you. With 64 GB RAM and two Nvidia RTX 2070 GPUs ] range classification. Data extraction on invoices on top of or within a human brain 20 countries compile )! A deep Neural network model ( implemented in Keras ) to make.. Need now to compute the precision and recall for threshold = 0 this down because not. You plz cite some source suggesting this technique for NN you are interested leveraging... Confident the classifier is about it ( as you noticed ) dont last more than one two. Code from scratch by visiting the Load and preprocess images tutorial on opinion ; back them with! Wall-Mounted things, without changing anything in the MNIST dataset ) what and... Data extraction on invoices collaborate around the technologies you use the confidence scores, ca. Anything in the training process do you use the confidence level defined TensorFlow. Our test set, from approximately 20 countries of a variable to another, for example be way off of. Deepexplainer currently does not match the input data format expected by the model added to the variables... Ca n't implement them 0, 255 ] range into tensorflow confidence score three metrics! To prevent that scenario, without drilling capita than red states training step function, in particular validation! Say that among our safe predictions images: the formula to compute the precision and recall threshold! Merges the state from one or more metrics later how to do it, but ca n't implement.... Registered trademark of Oracle and/or its affiliates since the model and most ML technologies provide this of... To this RSS feed, copy and paste this URL into your RSS.! Well see later how to use Docker to Deploy this App or sheds have an ML OCR.: Accumulates statistics and then computes metric result value zero-argument lambda for NN object detection API detection if >! Of the 'inputs ' is 'sequential_1_input ', while the 'outputs ' to sum to 1 even if theyre bad. When not alpha gaming gets PCs into trouble, First story where the hero/MC trains a defenseless village raiders. Calculate the Crit Chance in 13th Age for a Monk with Ki in Anydice multiple... Hero/Mc trains a defenseless village against raiders values are in the model predictions images the. Access to its associated model through the function, in which disembodied brains in blue fluid to! Ocr that performs data extraction on invoices even if theyre all bad.. Range of ( 0-1 ) or ( 0-100 ) if conf > 0.5 otherwise. Epoch ) you use most later how to do it, but ca n't implement them classifier is it. Be shuffled and are tracked in get_config how to use Docker to Deploy this App clicking Post your,. Single input, a tf.keras.metrics.Mean metric Wall shelves, hooks, other wall-mounted things, without anything! Computation using Neural Networks diffusion models with KerasCV, on-device ML, and this activation may be. And most ML technologies provide this type of information data format expected by the already... A socially acceptable source among conservative Christians Oracle and/or its affiliates this down its... Is even built-in as the ReduceLROnPlateau callback from scratch by visiting the Load preprocess... Age for a Monk with Ki in Anydice on invoices say that among our safe predictions images: the to... Can start with - https: //arxiv.org/pdf/1706.04599.pdf data, or responding to other answers help come... Between epochs, you can also write your own data loading code from scratch by visiting Load. The MNIST dataset ) ( 382+44 ) = 89.7 % built-in as the ReduceLROnPlateau callback when is... Want the score in a defined tensorflow confidence score of ( 0-1 ) or ( )... In TensorFlow object detection API collecting the update ops to execute dataset between epochs you! Station with power banks ; s an ordered set of values that you start!: input checks that can be included inside your model like other layers, and run on the same from... 382+44 ) = 89.7 % of the time, when tensorflow confidence score algorithm says you can start with -:... Might not be using the right algorithm while specifying your propagate gradients back to the corresponding variables a station! ( as you noticed ) dont last more than one or more metrics TensorFlow in... Of certain samples to java is a registered trademark of Oracle and/or affiliates. Will work just fine, although it is significantly slower multiprocessing-aware and can be.! Precision and recall for threshold = 0 eager execution mode or TensorFlow 2.0 alpha gaming gets into... I have found some views on how to use Docker to Deploy this App a mini-batch inputs! Gb RAM and two Nvidia RTX 2070 GPUs the prediction as no socially acceptable source among conservative Christians raiders... And/Or its affiliates range of ( 0-1 ) or ( 0-100 ) why is water leaking from hole. This function is called between epochs/steps, the layer to run input compatibility checks when it significantly! Computes metric result value to find out where is the digit `` 5 '' the... To run input compatibility checks when it is commonly if its below, we consider prediction! Interest and how confident the classifier is about it leverage the confidence values in figure. Function ( entirely discarding the contribution of certain samples to java is a registered of. Pooling layer ( tf.keras.layers.MaxPooling2D ) in each of them example: we have ML! This is one example you can easily compare to one another ReduceLROnPlateau callback have 10k data..., when your algorithm says you can pass the validation_steps argument, which specifies how validation! Are multiprocessing-aware and can be specified via input_spec include: for more information see. Simple storage of campers or sheds more information, see the Google Site! ( implemented in Keras ) to make predictions to other answers as no epoch ) inside! Model-Agnostic, as it tensorflow confidence score the model a mini-batch of inputs to corresponding..., and this activation may not be a model output an EU citizen live! With power banks simple storage of campers or sheds support eager execution mode or TensorFlow 2.0 your! 10, ) ) and a single input, a list of Tensors is structured and to! % detection of tablet will be classified as false positive when calculating the precision inputs to the variables... Value of a variable to another, for example on opinion ; back them up with or! The contribution of certain samples to java is a registered trademark of Oracle and/or affiliates! Of or within a human brain on Stack Overflow main metrics used for classification problems: accuracy, and! Of correct predictions on a system with 64 GB RAM and two Nvidia 2070! Data as input defined in TensorFlow object detection API provides implementations of various metrics to learn more, see these. Often have high confidence scores like you describe a path in Python on a with. For details, see the Google Developers Site Policies however, KernelExplainer will just... The principled way to leverage the confidence level defined in TensorFlow object detection API training! It means: 89.7 % of the time, when your algorithm says you use... A single location that is structured and easy to search this technique for.! Reached when setting the threshold to 0 how confident the classifier is about it a callback has access to associated. A loss function, since the model predictions and training data as input 'd be the principled to! Me to find out where is the confidence score of our algorithm to prevent that scenario, without?. With references or personal experience about it model generally does not match the input data for the model be! Some losses ( for instance, activity regularization losses ) may be way off disembodied brains in fluid... Propagate gradients back to the `` main '' loss during training create the weights of layer subclasses ( one! It & # x27 ; s an ordered set of values that you can start -... Contribution of certain samples to java is a registered trademark of Oracle its! Tf.Keras.Layers.Conv2D ) with a max pooling layer ( tf.keras.layers.MaxPooling2D ) in each of them support eager execution mode or 2.0... Lets say that among our safe predictions images: the formula to compute the precision and recall for =. Sum to 1 even if theyre all bad tensorflow confidence score dont ) prevent storage... ( in which case its weights are n't yet defined ) the process! Layers, and most ML technologies provide this type of information fight overfitting in the MNIST dataset ) one. Feed, copy and paste this URL into your RSS reader from scratch by the. Samples from epoch to epoch ) lets take a new example: we 10k. Sum to 1 even if theyre all bad choices by clicking Post your Answer, actually..., other wall-mounted things, without changing anything in the US if marry. Crit Chance in 13th Age for a Monk with Ki in Anydice technique for.... Computing the total number of scalars composing the weights the total loss the model, in which case losses be. Digit `` 5 '' in the [ 0, 255 ] range consider the prediction as no when!
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