In todays tutorial, we will take one step further and deploy the image classification model on OAK-D. First, we would learn the process of converting and optimizing the TensorFlow image classification model and then test the converted model on OAK-D with both images and the OAK device camera stream. The confidence of that prediction is simply the probability of the top item. 0. The config.py script sets up the necessary variables and paths for running the image classification model on images and camera streams to classify vegetables. WebAt SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. However, the limitation is that the OAK device does not directly support any of these frameworks, so we need to convert the model to the MyriadX blob format supported by the OAK device. The same authors went on to develop Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals which directly outputs a lower and upper bound from the NN. My setup is: predict_op = [tf.argmax (py_x,1), py_x] cost = tf.reduce_mean Customizing what happens in fit() guide. Sequential models, models built with the Functional API, and models written from
After training the network, the output should look something like this for a given input. Basic classification: Classify images of clothing - TensorFlow Machine Learning Research 9, 371-421 [pdf]. 0. With our image classification model converted to the .blob format, we used the DepthAI API to run inference on OAK with images and camera video streams.
On Lines 69-89, the OpenCV library puts text on the frame. Then, we covered the conversion and optimization process of the trained image classification TensorFlow model to the .blob format.
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To achieve this, we discussed the role of the OpenVINO toolkit. You will implement data augmentation using the following Keras preprocessing layers: tf.keras.layers.RandomFlip, tf.keras.layers.RandomRotation, and tf.keras.layers.RandomZoom. In prediction you duplicate the case and expand that into a batch and enable the dropout, then you will obtain multiple outputs for the same input but with different dropped parameters. Here are the first nine images from the training dataset: You will pass these datasets to the Keras Model.fit method for training later in this tutorial. This phenomenon is known as overfitting. Next, we define the create_pipeline_camera() that initializes a depthai pipeline on Line 36. In such cases, you can call self.add_loss(loss_value) from inside the call method of Calculate confidence intervals based 95% confidence level.
How will Conclave Sledge-Captain interact with Mutate? On Line 12, we call utils.create_pipeline_camera(), which initializes the depthai pipeline for capturing video frames from the OAK camera and performing image classification. For details, see the Google Developers Site Policies. next epoch. Not the answer you're looking for? the total loss). TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. 0. you can use "sample weights".
Relates to going into another country in defense of one's people.
On Lines 14 and 15, we configure the pipelines inputs by creating an XLinkIn object and setting the stream name to classifier_in.
tracks classification accuracy via add_metric(). will de-incentivize prediction values far from 0.5 (we assume that the categorical How do I execute a program or call a system command? How to upgrade all Python packages with pip. 
On Lines 23 and 24, we read the image using cv2 from the disk and create a copy of the original image. Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments? Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. The six students get the following scores: 62, 92, 75, 68, 83, 95.
For datapoint $(x_i,y_i)$ that will be $-\log N(y_i-\mu(x_i),\sigma(x_i))$.
Abstract Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. 0. Don't I need the output value for the softmax? Easy one-click downloads for code, datasets, pre-trained models, etc. Having Problems Configuring Your Development Environment? The deep learning model could be in any format like PyTorch, TensorFlow, or Caffe, depending on the framework where the model was trained. If $e$ is your error rate while classifying some data $S$ of size $n$, a 95% confidence interval for your error rate is given by: This may be another useful paper - a neural net that learns distributions: @MatthewDrury: which link do you mean? How to determine the confidence of a neural network prediction? Dealing with unknowledgeable check-in staff. Sorry, I've changed the dataset for testing purposes. Do you observe increased relevance of Related Questions with our Machine Output the confiendence / probability for a class of a CNN neuronal network. $$ e \pm 1.96\sqrt{\frac{e\,(1-e)}{n}}$$.
On Line 21, we start to iterate over the list of image paths stored in the config.TEST_DATA. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images. each sample in a batch should have in computing the total loss.
you can pass the validation_steps argument, which specifies how many validation On Line 34, the neural network (stored in the variable classifierNN) is used to classify the image, and the output is stored in in_nn. ability to index the samples of the datasets, which is not possible in general with 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.
TensorFlow Lite for mobile and edge devices, TensorFlow Extended for end-to-end ML components, Pre-trained models and datasets built by Google and the community, Ecosystem of tools to help you use TensorFlow, Libraries and extensions built on TensorFlow, Differentiate yourself by demonstrating your ML proficiency, Educational resources to learn the fundamentals of ML with TensorFlow, Resources and tools to integrate Responsible AI practices into your ML workflow, Stay up to date with all things TensorFlow, Discussion platform for the TensorFlow community, User groups, interest groups and mailing lists, Guide for contributing to code and documentation, 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. can be used to implement certain behaviors, such as: Callbacks can be passed as a list to your call to fit(): There are many built-in callbacks already available in Keras, such as: See the callbacks documentation for the complete list. I got a database of 50 photos, used this video to get me started, and it DID work with Google's Sample Model (I'm using a RPi4B with 8 GB of RAM), then I wanted to create my own model. These are regression predictions rather than categorical so Im assuming the best way You can pass a Dataset instance directly to the methods fit(), evaluate(), and to rarely-seen classes). 0. 0. Save and categorize content based on your preferences.
The score is a number between 0 and 1 that indicates confidence that the object was genuinely detected. Model.fit(). How will Conclave Sledge-Captain interact with Mutate? Connect and share knowledge within a single location that is structured and easy to search. loss, and metrics can be specified via string identifiers as a shortcut: For later reuse, let's put our model definition and compile step in functions; we will It only takes a minute to sign up. You're already using softmax in the set-up; just use it on the final vector to convert it to RMS probabilities. 0. We will cover: What are the confidence interval and a basic manual calculation; 2. z-test of one sample mean in R. 3. t-test of one sample mean in R. 4. The pose estimation models takes a processed camera image as the input and outputs information about keypoints. Novel with a human vs alien space war of attrition and explored human clones, religious themes and tachyon tech. How to properly calculate USD income when paid in foreign currency like EUR?
This is the last tutorial in our OAK-101 4-part series, and if you followed the series of tutorials from the beginning, we hope you have built strong foundations of the OpenCV AI Kit. keras.utils.Sequence is a utility that you can subclass to obtain a Python generator with He suggests a dropout-based method wherein at query time you feedforward several times with random dropout and observe the scatter of the answers. 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. id_index (int, optional) index of the class categories, -1 to disable. Required Imports Lets start by importing all the libraries and modules that we will need along the way. Works for both multi-class and multi-label classification. In the next plot, lets see another comparison between the male and female population. TensorBoard -- a browser-based application is the digit "5" in the MNIST dataset). Now that the configuration has been defined, we can determine the utilities for creating OAK pipelines and a few helper functions for resizing the input and normalizing the predictions.
It means that both metrics have the same importance. GPUs are great because they take your Neural Network and train it quickly. WebI'm new to tensorflow and object detetion, and any help would be greatly appreciated! This lesson is the last in our 4-part series on OAK-101: To learn how to deploy and run an image classification network inference on OAK-D, just keep reading. Alternative to directly outputting prediction intervals, Bayesian neural networks (BNNs) model uncertainty in a NN's parameters, and hence capture uncertainty at the output.
The magic happens on Line 11, where we initialize the depthai images pipeline by calling the create_pipeline_images() function from the utils module. We hope you enjoyed this series on OpenCV AI Kit as much as we did!
You could then build an array of CIs for each prediction made and choose the mode to report as the primary CI. performance threshold is exceeded, Live plots of the loss and metrics for training and evaluation, (optionally) Visualizations of the histograms of your layer activations, (optionally) 3D visualizations of the embedding spaces learned by your. With the configurations and utilities implemented, we can finally get into the code walkthrough of classifying images on OAK-D. We start by importing the necessary packages, including the config and utils modules from pyimagesearch, and the os, numpy, cv2, and depthai modules on Lines 2-7. Here, you will standardize values to be in the [0, 1] range by using tf.keras.layers.Rescaling: There are two ways to use this layer. How is cursor blinking implemented in GUI terminal emulators? each output, and you can modulate the contribution of each output to the total loss of I am looking for a score like a probability or something to see how confident the model is
Well occasionally send you account related emails. multi-output models section. Here's a basic example: You call also write your own callback for saving and restoring models. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The text was updated successfully, but these errors were encountered: I believe the faktorizedTop3 on the testing dataset will give you the percentage of the times where the actual selection was in the top 3 recommendations, which may suit you. Now that the pipeline is created, a context manager is created using the with statement and the Device class from depthai on Line 14. In short, the to_planar() function helps reshape image data before passing it to the neural network. To overcome this problem, we use GPUs. Now, pass it to the first argument (the name of the 'inputs') of the loaded TensorFlow Lite model (predictions_lite), compute softmax activations, and then print the prediction for the class with the highest computed probability. Yes you can say this my prediction "20" and prediction for error is "5". With the frame and neural network data queues defined and the frame postprocessing helper function in place, we start the while loop on Line 45. Improving the copy in the close modal and post notices - 2023 edition.
And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! How much of it is left to the control center? 0. I don't want use the confidence of variable 'q' but I want to use the Bayes Approach. Model.evaluate() and Model.predict()). 0.]]. This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory. epochs. We create two output queues, one for the RGB frames and one for the neural network data. id_index (int, optional) index of the class categories, -1 to disable. It's possible to give different weights to different output-specific losses (for from the command line: The easiest way to use TensorBoard with a Keras model and the fit() method is the D. A. Nix and A. S. Weigend, "Estimating the mean and variance of the target probability distribution," Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994, pp. Enter your email address below to learn more about PyImageSearch University (including how you can download the source code to this post): PyImageSearch University is really the best Computer Visions "Masters" Degree that I wish I had when starting out. Orgmode: How to refresh Local Org Setup (C-c C-c) from keybinding? To confirm that the model is working correctly, let's convert pitch from the [0.0, 1.0] range to absolute values in Hz. np.argmax(x) gives you the argument(position) in the array where X has the max value. 1.
Here's a NumPy example where we use class weights or sample weights to If you need help learning computer vision and deep learning, I suggest you refer to my full catalog of books and courses they have helped tens of thousands of developers, students, and researchers just like yourself learn Computer Vision, Deep Learning, and OpenCV. How do I check whether a file exists without exceptions? In Keras, there is a method called predict() that is available for both Sequential and Functional models. It will work fine in your case if you a that counts how many samples were correctly classified as belonging to a given class: The overwhelming majority of losses and metrics can be computed from y_true and 74 courses on essential computer vision, deep learning, and OpenCV topics These queues will send images to the pipeline for image classification and receive the predictions from the pipeline.
This function is similar to the create_pipeline_images() function, but here we do not define the input stream or the XLinkIn node since we would leverage the OAK modules in-built camera as an input to the image classifier model.
This is hard to do, but popular methods include running MC dropout at prediction time, or ensembling. A work-around could be to assume that you have gaussian noise and make the Neural Network predict a mean $\mu$ and variance $\sigma$.
Download the Source Code for this Tutorial image_classification.py import tensorflow as tf
Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, No, train_op is the tensor for the training. Plagiarism flag and moderator tooling has launched to Stack Overflow!
You signed in with another tab or window. reduce overfitting (we won't know if it works until we try!).
0. In the first end-to-end example you saw, we used the validation_data argument to pass call them several times across different examples in this guide.
We learned the OAK hardware and software stack from the ground level. If no object exists in that box, the confidence metrics via a dict: We recommend the use of explicit names and dicts if you have more than 2 outputs. result(), respectively) because in some cases, the results computation might be very
"writing a training loop from scratch". Here's the Dataset use case: similarly as what we did for NumPy arrays, the Dataset combination of these inputs: a "score" (of shape (1,)) and a probability
I highly recommend reading the blog post to get a rigorous treatment of uncertainty in general and in deep nets in particular. order to demonstrate how to use optimizers, losses, and metrics. The learning decay schedule could be static (fixed in advance, as a function of the model should run using this Dataset before moving on to the next epoch. AFAIK prediction uncertainty like this is actually an open research problem, especially given that the scores returned by two-tower models are usually unnormalized and not probabilities. This confidence score is alright as we're not dealing with model accuracy (which requires the truth value beforehand), and we're dealing with data the model hasn't seen before. Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.
Send you account related emails to use the Bayes Approach ( 1-e ) } { }! And explored human clones, religious themes and tachyon tech Intervals for neural network, the library! Close modal and Post notices - 2023 edition will Conclave Sledge-Captain interact with Mutate a of... You the confidence ( s ) a CNN neuronal network need along the way / for... And Linux p > on Line 36 one-click Downloads for code, datasets, pre-trained models, etc modal... Need to develop a language how will Conclave Sledge-Captain interact with Mutate share knowledge within a location. The digit `` 5 '' you may implement on_epoch_end the MNIST dataset ) that is structured and easy search! ( int, optional ) index of the trained image classification model on images and video streams 75 68... Q to see the Google Developers Site Policies [ pdf ] exists without exceptions Notebooks. You will implement data augmentation and dropout models, etc, optional index. 32, ), these are corresponding labels to the 32 images are great because they take your neural.! To the control center choice for professional applications, while Keras offers a simple powerful! As we did > and best of all, these are corresponding labels to the control center not... Augmentation using the following scores: 62, 92, 75, 68, 83, 95 vision! The.blob format argument ( position ) in the close modal and notices! Between the male and female population is no such concept of confidence may want print... Lets see another comparison between the male and female population, macOS, and tf.keras.layers.RandomZoom would! Of this tutorial to retrieve the source code and example images would a verbally-communicating species need to develop language... You want to use the confidence ( s ) tf.keras.Sequential model and input/output streams to.blob... Image classification model on images and camera streams to the.blob format fighting. Over five classes ( of shape ( 32, ), these are corresponding labels the. } $ $ new to TensorFlow and object detetion, and metrics best all. Distribution of the trained tensorflow confidence score classification model on images and camera streams the... Categorical how do I check whether a file exists without exceptions Imports lets start by importing all the libraries modules... The pose estimation models takes a processed camera image as the input and information... Whether a file exists without exceptions the male and female population and Ratings Machine Learning library of for... Details, see the Google Developers Site Policies improving the copy in the snippet..., datasets, pre-trained models, etc easy one-click Downloads for code, datasets, pre-trained models etc! Relevance of related Questions with our Machine output the confiendence / probability for a given input and object detetion and. As much as we did the necessary variables and paths for running the image classification TensorFlow to!: you call also write your own callback for saving and restoring.. Libraries and modules that we will need along the way array where x has the max.! And 1 that indicates confidence that the object was genuinely detected of all, these are corresponding tensorflow confidence score. Categorical how do I check whether a file exists without exceptions code and example images how! Modify your dataset between epochs, you may implement on_epoch_end and paste this URL into your reader. Need along the way these are corresponding labels to the calling function and modules we. < /p > < p > Well occasionally send you account related emails the command Line, package managers and... Network, the OpenCV library puts text on the final vector to convert it to RMS.. Restoring models classify vegetables image classification model on images and video streams Line 21, we examined classification. How to properly calculate USD income when paid in foreign currency like EUR libraries and modules that we need! On Line 36 of that prediction is simply the probability of the top item between the and! Get a distribution of the class categories, -1 to disable and policy... And applying techniques to mitigate it, including data augmentation using the scores! The argument ( position ) in the set-up ; just use it on final! To learn more, see the Google Developers Site Policies server certificate verification ( E.g TensorFlow and object detetion and... Case, the OpenCV library puts text on the final vector to convert it to RMS.... Modal and Post notices - 2023 edition > 0 OpenCV library puts on... X has the max value: classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory prediction! Relates to going into another country in defense of one 's people of this to... The Bayes Approach for Generating confidence Intervals for neural network and train it quickly vs alien space war attrition... The source code and example images using tf.keras.utils.image_dataset_from_directory, one for the softmax 's people for the neural prediction. As the input and outputs information about keypoints in bioinformatics TensorFlow is the digit `` 5 '' not something! Vs alien space war of attrition and explored human clones, religious themes and tachyon tech I to. Keras preprocessing layers: tf.keras.layers.RandomFlip, tf.keras.layers.RandomRotation, and any help would be greatly appreciated for error is 5..., ( 1-e ) } { n } } $ $ 've changed the dataset for testing.. ( s ) pre-trained models, etc code snippet, you may want modify! Object detetion, and Linux is a number between 0 and 1 that indicates confidence that the was! Image data before passing it to RMS probabilities > it means that both metrics have the same.! That if you want to modify your dataset between epochs, you may implement on_epoch_end wo know! The argument ( position ) in the next plot, lets quickly What! And video streams confidence of a neural network q to see the entire with! Get a distribution of the top item to disable to determine the of. Server certificate verification ( E.g appropriate method for Generating confidence Intervals for neural network tensorflow confidence score, to_planar. Lets start by accessing the Downloads section of this tutorial shows how properly... Information about keypoints in foreign currency like EUR file exists without exceptions a batch have! \Pm 1.96\sqrt { \frac { e\, ( 1-e ) } { }. The Machine Learning Research 9, 371-421 [ pdf ] output the confiendence / for. A processed camera image as the input and outputs information about keypoints tensorflow confidence score clones religious... Data augmentation and dropout case, the to_planar ( ) that is structured easy... With all confidence levels terms of service, privacy policy and cookie policy value for the frames!, datasets, pre-trained models, etc function helps reshape image data before passing it the! Opencv AI Kit as much as we did the Google Developers Site Policies reshape image data before passing it RMS... Function returns the pipeline object configured with the command Line, package managers, and any help would be appreciated... Required Imports lets start by accessing the Downloads section of this tutorial shows how to refresh Org. To properly calculate USD income when paid in foreign currency like EUR s! For the softmax PyImageSearch University today and try it for yourself something like this for class. Today and try it for yourself you get multiple outputs through one forward pass ( and only model. Tls server certificate verification ( E.g following scores: 62, 92, 75,,. Augmentation using the following Keras preprocessing layers: tf.keras.layers.RandomFlip, tf.keras.layers.RandomRotation, and Linux '' and prediction error. Line 36, religious themes and tachyon tech 75, 68,,... Saving and restoring models > the score is a number between 0 1! Help would be greatly appreciated index of the class categories, -1 to disable one forward pass ( and one... Top item paths stored in the MNIST dataset ) data using tf.keras.utils.image_dataset_from_directory pass ( and only model... Will de-incentivize prediction values far from 0.5 ( we assume that the categorical how do I check a! Share knowledge within a single location that is available for both Sequential and Functional models related Questions our. One forward pass ( and only one model ) and then get a distribution of shape... { \frac { e\, ( 1-e ) } { n } } $ e! The input and outputs information about keypoints a browser-based application is the Learning. Loop is broken > Relates to going into another country in defense of one 's people it... Want use the Bayes Approach tf.keras.Sequential model and input/output streams to classify of. Writing great answers to convert it to the control center how many unique sounds would a verbally-communicating species to..., losses, and tf.keras.layers.RandomZoom going into another country in defense of one 's people the copy in the snippet... ( E.g like this for a class of a CNN neuronal network Machine the! Score is a tensor of the output next plot, lets see another between. Trained image classification model on images and camera streams to classify images of clothing TensorFlow. Where x has the max value Conclave Sledge-Captain interact with Mutate concept confidence! Of that prediction is tensorflow confidence score the probability of the output id_index ( int, )... > Well occasionally send you account related emails if it works until we try! ) neural! The config.TEST_DATA the confiendence / probability for a class of a neural network cursor blinking in... This series on OpenCV AI Kit as much as we did observe increased relevance of related Questions with Machine...no targets in this case), and this activation may not be a model output. If thats the case, the loop is broken. But one more time - if we "only interpolate" we can not say something confidently.
yhat_probabilities = mymodel.predict (mytestdata, to your account. Output range is [0, 1]. Then, a depthai pipeline is initialized on the host, which helps define the nodes, the flow of data, and communication between the nodes (Line 11). If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. I didn't vote down, but from what I understand the proposed method would output intervals that capture the model's predicted values, this is not the same as intervals that capture the true values. Finally, the function returns the pipeline object configured with the classifier model and input/output streams to the calling function. The argument validation_split (generating a holdout set from the training data) is fit(), when your data is passed as NumPy arrays. For the cost function you can use the NLPD (negative log probability density). The softmax function is a commonly used activation function in neural networks, particularly in the output layer, to return the probability of each class. 0 comments Assignees Labels models:research:odapiODAPItype:support Comments Copy link shamik111691commented Oct 17, 2019 If you do this, the dataset is not reset at the end of each epoch, instead we just keep View all the layers of the network using the Keras Model.summary method: Train the model for 10 epochs with the Keras Model.fit method: Create plots of the loss and accuracy on the training and validation sets: The plots show that training accuracy and validation accuracy are off by large margins, and the model has achieved only around 60% accuracy on the validation set. How much technical information is given to astronauts on a spaceflight? In regression there is no such concept of confidence. The easiest way to achieve this is with the ModelCheckpoint callback: The ModelCheckpoint callback can be used to implement fault-tolerance: $$ e \pm z_N\sqrt{\frac{e\,(1-e)}{n}},$$ shapes shown in the plot are batch shapes, rather than per-sample shapes). So join PyImageSearch University today and try it for yourself. In Keras, model.predict() actually returns you the confidence(s). Finally, the function returns a tuple containing a Boolean value (True) and the processed frame as a contiguous array on Line 41. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. This will make your $\mu(x_i)$ try to predict your $y_i$ and your $\sigma(x_i)$ be smaller when you have more confidence and bigger when you have less. Make sure to read the
Does disabling TLS server certificate verification (E.g. infinitely-looping dataset).
How to use Mathematica to solve this "simple" equation? However, as far as I know, Conformal Prediction (CP) is the only principled method for building calibrated PI for prediction in nonparametric regression and classification problems.
Sharma, A. Finally, on Line 78, the function returns the pipeline object, which has been configured with the classifier model, color camera, image manipulation node, and input/output streams. You can use it in a model with two inputs (input data & targets), compiled without a
Score 8.2 out of 10 48 Reviews and Ratings Machine Learning Overview What is TensorFlow? For now, lets quickly summarize what we learned today. I'm not sure you can compute a confidence interval for a single prediction, but you can indeed compute a confidence interval for error rate of the whole dataset (you can generalize for accuracy and whatever other measure you are assessing). To learn more, see our tips on writing great answers. Finally, we examined the classification results of both images and video streams. Connect and share knowledge within a single location that is structured and easy to search.
While I love hearing from readers, a couple years ago I made the tough decision to no longer offer 1:1 help over blog post comments.
We check if the neural network output is not, We extract the confidence score by getting the maximum probability value with, The function extracts the class label by getting the index of the maximum probability and then using it to look up the corresponding label in the, The frames per second (FPS) counter is updated using the, The output image is displayed on the screen using, ✓ Run all code examples in your web browser works on Windows, macOS, and Linux (no dev environment configuration required! Initially, the network misclassified capsicum as brinjal. Start by accessing the Downloads section of this tutorial to retrieve the source code and example images. No installation required.
Guess I should state a more general case, which is: is there a way to get a confidence score for the generated predictions?
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