The purpose of this project is to classify Kaggle Consumer Finance Complaints into 11 classes. Multiclass Classification with Image Augmentation. Explore and run machine learning code with Kaggle Notebooks | Using data from Rock Paper Scissors Dataset Leaderboard log loss for this model is 1.19736, which is a 12.02% decrease in log loss. Due to time and computational cost it was not possible for me to run more experiments using different known architectures other than VGG-16 such as RESNET and Inception V-3 for this dataset. I was implementing a multi-class image classification neural network in Keras (v2.4.3). After that we flatten our data and add our additional 3 (or more) hidden layers. Data leakage is an issue in this problem because most images look very very similar as they are just frames from videos. Then after we have created and compiled our model, we fit our training and validation data to it with the specifications we mentioned earlier. Training data was also shuffled during training the model, while validation data was used to get the validation accuracy and validation loss during training. For each experiment only the best model was saved along with their weights(a model only gets saved per epoch if it shows higher validation accuracy than the previous epoch ). Vertical flipping also does not make sense because the camera is in a fixed position and companies wouldn’t capture boats photos up-side-down. Activation layers apply a non-linear operation to the output of the other layers such as convolutional layers or dense layers. To use classification metrics, we had to convert our testing data into a different numpy format, numpy array, to read. The goal is to predict the likelihood that a fish is from a certain class from the provided classes, thus making it a multi-class classification problem in machine learning terms. However, due to computational costs, it may not be possible to run the transfer learning model with VGG-16 architecture for sufficient number of epochs so that it may be able to converge. The GitHub is linked at the end. Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. It is not feasible to discuss every block of code in this story. Made changes in the following codes . Explore and run machine learning code with Kaggle Notebooks | Using data from [Private Datasource] After that the images were split into a training set and a validation set. Kaggle Competition | Multi class classification on Image and Data Published on March 29, 2019 March 29, 2019 • 13 Likes • 0 Comments The leaderboard log-loss is 1.19, so the log-loss is quite close. But thankfully since you only need to convert the image pixels to numbers only once, you only have to do the next step for each training, validation and testing only once- unless you have deleted or corrupted the bottleneck file. As the input is just raw images(3-dimensional arrays with height x width x channels for computers) it’d be important to preprocess them for classifying them into provided labels. Confusion matrix works best on dataframes. I'd like to evaluate the performance of my model after being compiled and fitted. Perhaps, the fishing boats should make some area in their boats as a reference point too for faster classification. Kamal khumar. #This is the best model we found. Additionally, batch normalization can be interpreted as doing preprocessing at every layer of the network, but integrated into the network itself. How do you use machine learning with fishes? People don’t realize the wide variety of machine learning problems which can exist.I, on the other hand, love exploring different variety of problems and sharing my learning with the community here.Previously, I shared my learnings on Genetic algorithms with the community. Kaggle Competition: Product Classification Machine Learning CS933 Term Project Name: Muping He Jianan Duan Sinian Zheng Acknowledgements : These are the complete, official rules for the Competition (the 'Competition Rules') and incorporate by reference the contents of the Competition Website listed above. Image segmentation 3. This data would be used to train our machine about the different types of images we have. So the reasonable score for beating the KNN benchmark would be anything <1.65074 even if the difference is not large considering running the neural network longer would keep lowering the loss. The testing data can also just contain images from Google that you have downloaded, as long as it make sense to the topic you are classifying. The metric used for this Kaggle competition is multi-class logarithmic loss (also known as categorical cross entropy). For neural networks, this is a key step. As the pre-trained networks have already learnt how to identify lower level features such as edges, lines, curves etc with the convolutional layers which is often the most computationally time consuming parts of the process, using those weights help the network to converge to a good score faster than training from scratch. Both elephants and horses are rather big animals, so their pixel distribution may have been similar. It’s definitely possible that a different architecture would be more effective. Data: Kaggle … The dataset features 8 different classes of fish collected from the raw footage from a dozen different fishing boats under different lighting conditions and different activity, however it’s real life data so any system for fish classification must be able to handle this sort of footage.Training set includes about 3777 labeled images and the testing set has 1000 images. This final model has the loss of around 1.19736 in the leaderboard, beating the former one by 12.02% and sending me in the top 45% of the leaderboard for the first time. Networks that use Batch Normalization are significantly more robust to bad initialization. Is Apache Airflow 2.0 good enough for current data engineering needs? A perfect classifier will have the log-loss of 0. Batch Normalization : Batch Normalization is a A recently developed technique by Ioffe and Szegedy which tries to properly initializing neural networks by explicitly forcing the activations throughout a network to take on a unit gaussian distribution at the beginning of the training. This means that the tagging algorithm is capable of learning based on our input and make better classifications in the future. In practice we put the Batchnorm layers right after Dense or convolutional layers. This step is fully customizable to what you want. This inspires me to build an image classification model to mitigate those challenges. There are lots on online tutorial on how to make great confusion matrix. On top of that, images were of different sizes and similar sized images had similar labels(shot from same camera in the same time), to overcome that issue, resizing each images was important. The testing data set would contain the rest of the data in an unlabeled format. A well-designed convolutional neural network should be able to beat the random choice baseline model easily considering even the KNN model clearly surpasses the initial benchmark. On the other hand, if the class label is 1(the instance is from that class) and the predicted probability is close to 0(the classifier is confident in its mistake), as log(0) is undefined it approaches ∞ so theoretically the loss can approach infinity. Finally, we create an evaluation step, to check for the accuracy of our model training set versus validation set. One of them is the classification metrics and the other is the confusion matrix. Validation data set would contain 5–10% of the total labeled data. The second cell block takes in the converted code and run it through the built in classification metrics to give us a neat result. Then we simply tell our program where each images are located in our storage so the machine knows where is what. Then, please follow the Kaggle installation to obtain access to Kaggle’s … For example, speed camera uses computer vision to take pictures of license plate of cars who are going above the speeding limit and match the license plate number with their known database to send the ticket to. Deep learning based techniques (CNNs)has been very popular in the last few years where they consistently outperformed traditional approaches for feature extraction to the point of winning imagenet challenges. The model was built with Convolutional Neural Network (CNN) and Word Embeddings on Tensorflow. When you upload an album with people in them and tag them in Facebook, the tag algorithm breaks down the person’s picture pixel location and store it in the database. As we can see in our standardized data, our machine is pretty good at classifying which animal is what. As the classes were heavily imbalanced, one of my hypotheses is if I generate more photos with data augmentation for the classes that have less data than the others, save them and reach around 1000 images for each class, this model will be even more robust. Almost 50% of the world depends on seafood for their main source of protein. We also see the trend where the validation loss keeps decreasing initially but after around 2 epochs training loss keeps decreasing/accuracy keeps increasing, while the validation loss keeps increasing instead of decreasing. Here each image has been labeled with one true class and for each image a set of predicted probabilities should be submitted. However, even if having access to hours of raw footage is useful, according to TNC, for a 10 hour long trip, reviewing the footage manually takes around 6 hours for reviewers. Histograms represent the color distribution of an image by plotting the frequencies of each pixel values in the 3 color channels. In order to avoid the extremes of the log function, predicted probabilities are replaced with max(min(p,1−10^15),10^15). When you upload an album with people in them and tag them in Facebook, the tag algorithm breaks down the person’s picture pixel location and store it in the database. data visualization , classification , feature engineering 46 This testing data will be used to test how well our machine can classify data it has never seen. The validation curve most likely will converge to the training curve over sufficient number of epochs. TensorFlow patch_camelyon Medical Images– This medical image classification dataset comes from the TensorFlow website. My fully connected model on CNN features yielded a 3.10 score only, even if it had the same structure as original VGG-16’s fully connected model except with more dropout. Chickens were misclassified as butterflies most likely due to the many different types of pattern on butterflies. The goal of this project is to classify Kaggle San Francisco Crime Description into 39 classes. In this project, transfer learning along with data augmentation will be used to train a convolutional neural network to classify images of fish to their respective classes. To automate this process, TNC partnered with Kaggle to ask machine learning practitioners to build a system that automatically detects and classifies fishes from the video footage data with a $150,000 prize to offset the costs involved in training deep convolutional neural network. kaggle datasets download -d sriramr/fruits-fresh … The first step is to gather the data. I think UCI has many multi class datasets as example wine, glass, seeds, sat images. In the above equation, if the class label is 1(the instance is from that class) and the predicted probability is near to 1(classifier predictions are correct), then the loss is really low as log(x) → 0 as x →1 , so this instance contributes a small amount of loss to the total loss and if this occurs for every single instance(the classifiers is accurate) then the total loss will also approach 0. I had to use aggressive dropout in my models because of lack of computational resources, otherwise the models tended to crash my machine while running. Given enough time and computational power, I’d definitely like to explore the different approaches. Participants of similar image classification challenges in Kaggle such as Diabetic Retinopathy , Right Whale detection (which is also a marine dataset) has also used transfer learning successfully. I applied batch normalization in the model to prevent arbitrary large weights in the intermediate layers as the batch normalization normalizes the intermediate layers thus helping to converge well.Even in the model with batch-normalization enabled during some epochs training accuracy was much higher than validation accuracy, often going near 100% accurate. The Nature Conservancy Fishery Monitoring competition has attracted the attention of the contestants and have been featured in publications such as Engadget ,Guardian and Fortune. This is importing the transfer learning aspect of the convolutional neural network. We will not focus on the AI aspect, but rather on the simplest way to make an image classification algorithm. Notice it says that its testing on test_data. Fortunately many such networks such as RESNET, Inception-V3, VGG-16 pretrained on imagenet challenge is available for use publicly and I’ll be using one of them VGG-16, created by Oxford’s Visual Geometry Group for this competition. Multiclass log-loss punishes the classifiers which are confident about an incorrect prediction. Each image has only one fish category, except that there are sometimes very small fish in the pictures that are used as bait. An epoch is how many times the model trains on our whole data set. It is also best for loss to be categorical crossenthropy but everything else in model.compile can be changed. However, for a simple neural network project, it is sufficient. According to Fortune report on current usage of artificial intelligence in fishing industry , big fishing companies such as Luen Thai fishing reports that fishing operators in the pacific region typically sends a physical observer to accompany fishermen about 10 times out of 200 times in a year, however, this is clearly insufficient as there’s no one to monitor what is going on in the other 190 trips per boat. Only after applying batch normalization instead of the VGG-style fully connected model I saw significant improvement, and so I used it with the VGG architecture and applied data augmentation with it. However histograms completely ignore the shape,texture and the spatial information in the images and very sensitive to noise, so they can’t be used to train an advanced model. There are many transfer learning model. Multi-class classification The competition is multi-class classification problem. For this part, I will not post a picture so you can find out your own results. Making an image classification model was a good start, but I wanted to expand my horizons to take on a more challenging tas… This is our model now training the data and then validating it. Success in any field can be distilled into a set of small rules and fundamentals that produce great results when coupled together. Participants of similar image classification challenges in Kaggle such as Diabetic Retinopathy, Right Whale detection (which is also a marine … Unfortunately enough, the model with data augmentation is computationally expensive and takes around 1 hour per epoch on my machine, so I’ve trained the model only for 5 epochs(as it’s transer learning we have pre-trained weights already) and the end validation accuracy is 85%. We know that the machineâ s perception of an image is completely different from what we see. Data Augmentation alters our training batches by applying random rotations, cropping, flipping, shifting, shearing etc. Creation of the weights and feature using VGG16: Since we are making a simple image classifier, there is no need to change the default settings. This sort of problems can probably be overcome by adding more data for the other classes, either via data augmentation or by collecting real video footage again. Let’s import all the necessary libraries first: In this step, we are defining the dimensions of the image. A csv fileMovieGenre.csv can be downloaded. In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. I didn’t do it this time because with 8 class the training set would be around 8000 images. Since its a image classification contest where the categories are not strictly taken from the imagenet categories(e.g cats and dogs), and the domain is very novel and practical, I believe it’s a decent score. Eight target classes are provided in this dataset : Albacore tuna, Bigeye tuna, Yellowfin tuna, Mahi Mahi, Opah, Sharks, Other (meaning that there are fish present but not in the above categories), and No Fish (meaning that no fish is in the picture). A more realistic example of image classification would be Facebook tagging algorithm. Finetuning refers to the process of training the last few or more layers of the pretrained network on the new dataset to adjust the weight. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. The data is news data and labels (classes) are the degree of news popularity. If you don’t have Kaggle account, please register one at Kaggle. Batch can be explained as taking in small amounts, train and take some more. Here weights from a convolutional neural network pretrained on imagenet dataset is finetuned to classify fishes. To come to the point of using Data Augmentation, I had to extract the CNN features first and experiment with running different versions top layers on the CNN features. Ask Question Asked 3 years, 2 months ago. The only important code functionality there would be the ‘if normalize’ line as it standardizes the data. After completing this step-by-step tutorial, you will know: How to load data from CSV and make it available to Keras. Step 3 : Convert those videos to image snapshots. Creators of the original VGGNet subtracted the mean of each channel (R,G,B) first so the data for each channel had a mean of 0. Creating a bottleneck file for the training data. Once the files have been converted and saved to the bottleneck file, we load them and prepare them for our convolutional neural network. In this we’ll be using Colour Classification Dataset. Depending on your image size, you can change it but we found best that 224, 224 works best. Furthermore, their processing software expected input in (B,G,R) order whereas python by default expects (R,G,B), so the images had to be converted from RGB -> BGR. The goal is to train a CNN that would be able to classify fishes into these eight classes. However, if you are working with larger image files, it is best to use more layers, so I recommend resnet50, which contains 50 convolutional layers. It should be noted that this competition uses all the test dataset for the public leaderboard, unlike most competitions where the public leaderboard scores are only shown for a subset of the test dataset. Once we run this, it will take from half hours to several hours depending on the numbers of classifications and how many images per classifications. With a good GPU I’d probably be able to go to at least 90% accuracy by simply running the model for a few more epochs. In this tutorial, you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems. To combat the problem of proper monitoring, The Nature Conservancy , a global nonprofit fighting environmental problems has decided to create a technological solution by installing electronic monitoring devices such as camera, sensors and GPS devices to record all activities on board to check if they are doing anything illegal. Although this is more related to Object Character Recognition than Image Classification, both uses computer vision and neural networks as a base to work. Initially the baselines with random choice and K-nearest neighbors were implemented for comparison. It appeared the model predicted ALB and YFT to most of the incorrect images which are the dominant classes in the provided training set. This submission yields 2.41669 log-loss in the Kaggle leaderboard. This yields 1.65074 log-loss in the submission leaderboard. (I think it’s because this model used too much dropout resulting in a loss of information.). As I’ve recorded the accuracy and loss of the models per epoch, the final model can be compared to the second best alternative. If your dataset is not labeled, this can be be time consuming as you would have to manually create new labels for each categories of images. Preprocessing operations such as subtracting the mean of each of the channels as mentioned previously was performed and VGG-16 architecture without the last fully connected layers was used to extract the convolutional features from the preprocessed images. As we can see the training accuracy is near 100% in the diagram and the loss is near 0. Note that the benchmark model with k-nearest neighbors is also trained with the color histograms as features. Note that instead of using train_test_split methods in scikit-learn I randomly took 0.8% of each classes from the training set to the validation set while preserving the directory structure. Because each picture has its own unique pixel location, it is relatively easy for the algorithm to realize who is who based on previous pictures located in the database. The model in it’s current conditions, seems to be pretty good at classifying most of the classes aside from BET and LAG which are also the classes where the least amount of image data was provided. We will start with the Boat Dataset from Kaggle to understand the multiclass image classification problem. Winner of the ImageNet ILSVRC-2014 competition, VGGNet was invented by Oxford’s Visual Geometry Group , The VGG architecture is composed entirely of 3x3 convolutional and maxpooling layers, with a fully connected block at the end. Obvious suspects are image classification and text classification, where a document can have multiple topics. Take a look. 1. In the validation data out of 758 images, 664 images are classified accurately and 94 images are incorrect. In the specific dataset, random cropping does not make sense because the fish is already small compared to the whole photo and cropping the photos might create a situation where the model starts inferring most of the photo as ‘no fish’ class because the fish was cropped away during data augmentation. Fortunately the final model performed decently on the leaderboard, sending me to top 45% of the participants, which is my best one so far. On top of hectic conditions on a fishing boat, poor weather conditions such as insufficient light, raindrops hitting the camera lenses and people obstructing the view of fishes, often by choice, makes this task even harder for a human reviewer. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. Random choice : We predict equal probability for a fish to belong to any class of the eight classes for the naive benchmark. CNNs generally perform better with more data as it prevents overfitting. Please note that unless you manually label your classes here, you will get 0–5 as the classes instead of the animals. Because normalization greatly reduces the ability of a small number of outlying inputs to over-influence the training, it also tends to reduce overfitting. Golden Retriever image taken from unsplash.com. However, its possible that Kaggle provided an imbalanced dataset because it’s the accurate reflection of the volume of fishes in that marine area where ALB/YFT, both of them being tuna’s will be caught more, while Shark’s are considered endangered so they will be caught less. It preserves the distribution of the classes as visualized below. Follow. Identifying dog breeds is an interesting computer vision problem due to fine-scale differences that visually separate dog breeds from one another. Data Augmentation : Data augmentation is a regularization technique where we produce more images from the training data provided with random jitter, crop, rotate, reflect, scaling etc to change the pixels while keeping the labels intact. I’ve also added horizontal flipping and random shifting up and down and side by side because all these scenarios are likely. There’s another version of VGG, namely VGG-19 with very similar level of accuracy, however using it is more computationally expensive so I’d not be using it. To recap, the best model so far uses transfer learning technique along with data augmentation and batch normalization to prevent overfitting. Machine learning and image classification is no different, and engineers can showcase best practices by taking part in competitions like Kaggle… This dataset contains about 1,500 pictures of boats of different types: buoys, cruise ships, ferry boats, freight boats, gondolas, inflatable boats, kayaks, paper boats, and sailboats. In image classification histograms can be used as a feature vector with the assumption that similar images will have similar color distribution. Here is a great blog on medium that explains what each of those are. For reaching into this end to end solution, I’ve tried to progressively use more complex models to classify the images. An epoch is how many times the model tensor format step by.. And activation aspect of the capstone project of my Udacity machine learning competitions 2.41669 log-loss in the 3 channels... 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