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 set we worked with can be found here: animal-10 dataset. One of them is the classification metrics and the other is the confusion matrix. 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. Made changes in the following codes . For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. I’ve also added horizontal flipping and random shifting up and down and side by side because all these scenarios are likely. 7 min read. Make learning your daily ritual. 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. 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%. Step 1 : Catch the fishes in a fishing boat. 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. 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. beginner, deep learning, classification, +1 more multiclass classification Perhaps, the fishing boats should make some area in their boats as a reference point too for faster classification. This will lead to errors in classification, so you may want to check manually after each run, and this is where it becomes time consuming. Transfer learning refers to the process of using the weights a pretrained network trained on a large dataset applied to a different dataset (either as a feature extractor or by finetuning the network ). Random choice : We predict equal probability for a fish to belong to any class of the eight classes for the naive benchmark. 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. With data augmentation, each epoch with only 3777 training images takes me around 1 hour on my laptop, training on 8000 images would likely take 2.5x the time where each of the batches would even be slightly altered by keras when I’m using data augmentation, which takes some more time. How do you use machine learning with fishes? Are you working with image data? We will start with the Boat Dataset from Kaggle to understand the multiclass image classification problem. I particularly like VGG16 as it uses only 11 convolutional layers and pretty easy to work with. This is a multi-class text classification (sentence classification) problem. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. The only important code functionality there would be the ‘if normalize’ line as it standardizes the data. Initially the baselines with random choice and K-nearest neighbors were implemented for comparison. I’d have had to resize for feeding them into CNN in any case, however, resizing also was important to avoid data leakage issues. 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. For the final model I used the base model of VGG16 excluding the fully connected layers along with the pretrained weights, added a new Dense layer with dropout and batch normalization on top of it to predict the final images. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Activation layers apply a non-linear operation to the output of the other layers such as convolutional layers or dense layers. The data is news data and labels (classes) are the degree of news popularity. The GitHub is linked at the end. TensorFlow patch_camelyon Medical Images– This medical image classification dataset comes from the TensorFlow website. 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 pictures below will show the accuracy and loss of our data set. To create the dataset, TNC compiled hours of boating footage and then sliced the video into around 5000 images which contains fish photos captured from various angles.The dataset was labeled by identifying objects in the image such as tuna, shark, turtle, boats without any fishes on deck and boats with other small bait fishes. Here is what I did. Each image has only one fish category, except that there are sometimes very small fish in the pictures that are used as bait. This will be used to convert all image pixels in to their number (numpy array) correspondent and store it in our storage system. Success in any field can be distilled into a set of small rules and fundamentals that produce great results when coupled together. Take a look. 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. 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. This is importing the transfer learning aspect of the convolutional neural network. Is Apache Airflow 2.0 good enough for current data engineering needs? 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. Fortunately the final model performed decently on the leaderboard, sending me to top 45% of the participants, which is my best one so far. That is all the first line of code is doing. Fortune report on current usage of artificial intelligence in fishing industry, The Nature Conservancy Fishery Monitoring, http://www.exegetic.biz/blog/wp-content/uploads/2015/12/log-loss-curve.png, http://cs231n.github.io/transfer-learning/, https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html, Building a Credit Card Recommender and deploying on web and Chatbot Platform, Question Answering with Pretrained Transformers Using Pytorch, The 10 best new features in Scikit-Learn 0.24 , Natural Language Generation (Practical Guide), Keystroke Dynamics Analysis and Prediction — Part 1 (EDA), Predicting House Prices with Machine Learning. Multiclass log-loss punishes the classifiers which are confident about an incorrect prediction. 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. 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. Transfer learning is handy because it comes with pre-made neural networks and other necessary components that we would otherwise have to create. 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. To use classification metrics, we had to convert our testing data into a different numpy format, numpy array, to read. This article explains the basics of multiclass image classification and how to perform image augmentation. A table with all the experiments performed is given below along with their results. The 3rd cell block with multiple iterative codes is purely for color visuals. 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. Today we’ll create a multiclass classification model which will classify images into multiple categories. Of course the algorithm can make mistake from time to time, but the more you correct it, the better it will be at identifying your friends and automatically tag them for you when you upload. Batch can be explained as taking in small amounts, train and take some more. I didn’t do it this time because with 8 class the training set would be around 8000 images. The aim of this capstone project is to build a convolutional neural network that classifies different species of fishes while working reasonably well under constraints of computation. Please clone the data set from Kaggle using the following command. This step is fully customizable to what you want. The confusion matrix(non-normalized) plot of the predictions on the validation data is given below. 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. 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. Recursion Cellular Image Classification – This data comes from the Recursion 2019 challenge. 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. However, the Facebook tag algorithm is built with artificial intelligence in mind. Confusion matrix works best on dataframes. Jupyter is taking a big overhaul in Visual Studio Code. In this we’ll be using Colour Classification Dataset. Remember that the data must be labeled. On the extracted features(CNN codes), a small fully connected model was applied first but unfortunately it didn’t have a good result. Multi class Image classification using CNN and SVM on a Kaggle data set. 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. 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. It is also best for loss to be categorical crossenthropy but everything else in model.compile can be changed. 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. Now to make a confusion matrix. However, the GitHub link will be right below so feel free to download our code and see how well it compares to yours. The only difference between our model and Facebook’s will be that ours cannot learn from it’s mistake unless we fix it. I’ve added random rotation because it’s possible the camera’s are going to move from one corner to another to cover a broad area. 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. Code for visualization of the Accuracy and Loss: This picture below shows how well the machine we just made can predict against unseen data. Even if the quality of this dataset is quite high, given it shows the raw data from real video footage of fishermen in the boats, I’m uncertain if this dataset is a “comprehensive” representation of the fishing data the system would face in real life because of small changes such as weather differences, boat color, fishermen from different nationality wearing different ethnocentric clothes or with different skin color can easily offset the model as the background will be changed. Kaggle Competition | Multi class classification on Image and Data Published on March 29, 2019 March 29, 2019 • 13 Likes • 0 Comments N is the number of images in the test set, M is the number of image class labels, log is the natural logarithm, Yij is 1 if observation belongs to class and 0 otherwise, and P(Yij) is the predicted probability that observation belongs to class . 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. Although this is more related to Object Character Recognition than Image Classification, both uses computer vision and neural networks as a base to work. Explore and run machine learning code with Kaggle Notebooks | Using data from Rock Paper Scissors Dataset This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. The pretrained model is available in Caffe, Torch, Keras, Tensorflow and many other popular DL libraries for public use. Image classification sample solution overview. 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. As seen from the confusion matrix, this model is really good at predicting ALB and YFT classes(Albacore Tuna and YellowFin Tuna) respectively, presumably because the training data provided by Kaggle itself has more ALB and YFT photos than other classes. An epoch is how many times the model trains on our whole data set. This yields 1.65074 log-loss in the submission leaderboard. Data Augmentation alters our training batches by applying random rotations, cropping, flipping, shifting, shearing etc. To recap, the best model so far uses transfer learning technique along with data augmentation and batch normalization to prevent overfitting. Thankfully, Kaggle has labeled images that we can easily download. Source :cios233 community. #This is the best model we found. Similarly the validation accuracy is also near 95% while the validation loss is around 0.2% near the end of the 10 epochs. Another method is to create new labels and only move 100 pictures into their proper labels, and create a classifier like the one we will and have that machine classify the images. In the plot of the accuracy and loss for this model per epoch, it’s also seen that the training accuracy/loss is converging with the validation one per epoch(reproduction and further comparison on that in the free-form visualization section).I’ve ran the model for around 5/6 hours for training where each epoch was taking me around 1 hour. (I think it’s because this model used too much dropout resulting in a loss of information.). Chickens were misclassified as butterflies most likely due to the many different types of pattern on butterflies. Machine learning and image classification is no different, and engineers can showcase best practices by taking part in competitions like Kaggle… There are two great methods to see how well your machine can predict or classify. 7 min read. 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. Additionally, batch normalization can be interpreted as doing preprocessing at every layer of the network, but integrated into the network itself. Depending on your image size, you can change it but we found best that 224, 224 works best. Active 5 months ago. However,this model accurately identifies 35 sharks out of the 36 sharks in the validation set, despite them being rare. The model was built with Convolutional Neural Network (CNN) and Word Embeddings on Tensorflow. To visualize, here is the final model’s accuracy/loss chart over 5 epochs. 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. Keras ImageDataGenerators generate training data from the directories/numpy arrays in batches and processes them with their labels. Multi-class classification The competition is multi-class classification problem. 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. I mentioned in the proposal that I’d be trying a support vector machine model on the CNN extracted features, however later it seemed that’d result in too many weaker models and since the aim of the project is to establish a transfer learning model, it’s better to focus on that more. It preserves the distribution of the classes as visualized below. Golden Retriever image taken from unsplash.com. Both elephants and horses are rather big animals, so their pixel distribution may have been similar. Graphically[¹] , assuming the ith instance belongs to class j and Yij= 1 , it’s shown that when the predicted probability approaches 0, loss can be very large. Data: Kaggle … 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. For neural networks, this is a key step. Ours is a variation of some we found online. 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. Now, we can train and validate the model. Remember to repeat this step for validation and testing set as well. This model was built with CNN, RNN (LSTM and GRU) and Word Embeddings on Tensorflow. After that I applied dropout and batch normalization to the fully connected layer which beat the K-nearest benchmark by 17.50. Explore and run machine learning code with Kaggle Notebooks | Using data from [Private Datasource] The fish dataset was labeled by TNC by identifying objects in the image such as tuna, opah, shark, turtle, boats without any fishes on deck and boats with other fishes and small baits. In order to do so, let us first understand the problem at hand and then discuss the ways to overcome those. This means that the tagging algorithm is capable of learning based on our input and make better classifications in the future. How to do multi-class image classification in keras? 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. To validate the model I generated predictions for the validation data which had an accuracy score of 84.82% and a log loss of 1.0071. This submission yields 2.41669 log-loss in the Kaggle leaderboard. I was implementing a multi-class image classification neural network in Keras (v2.4.3). However, you can add different features such as image rotation, transformation, reflection and distortion. The normalized confusion matrix plot of the predictions on the validation set is given here. But since this is a labeled categorical classification, the final activation must always be softmax. In this dataset input images also come in different sizes and resolutions, so they were resized to 150 x 150 x 3 to reduce size.Dataset given by Kaggle does not have any validation set, so it was split into a training set and a validation set for evaluation. Leaderboard log loss for this model is 1.19736, which is a 12.02% decrease in log loss. Friedrich_Cheng94. Now that we have our datasets stored safely in our computer or cloud, let’s make sure we have a training data set, a validation data set, and a testing data set. To train a CNN model from scratch successfully, the dataset needs to be huge(which is definitely not the case here, the provided dataset from Kaggle is very small, only 3777 images for training) and machines with higher computational power is needed, preferably with GPU, which I don’t have access to at this point. There are many transfer learning model. Our engineers maintain these Docker images so that our users don’t need to worry about installation and dependency management, a huge barrier to getting started with data science. This is our model now training the data and then validating it. Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References) Posted November 19, 2020. 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. We found that this set of pairing was optimal for our machine learning models but again, depending on the number of images that needs to be adjusted. And that is the summary of the capstone project of my Udacity Machine Learning Nanodegree. The purpose of this project is to classify Kaggle Consumer Finance Complaints into 11 classes. Making an image classification model was a good start, but I wanted to expand my horizons to take on a more challenging tas… Object detection 2. This models performance on the test set in the leaderboard is only 1.36175, which is worse than the final models performance over only 5 epochs. 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. 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. data visualization , classification , feature engineering 46 Here each image has been labeled with one true class and for each image a set of predicted probabilities should be submitted. 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. It contains the following information for each movie: IMDB Id, IMDB Link, Title, IMDB Score, Genre and a link to download the movie poster. Computer vision and neural networks are the hot new IT of machine learning techniques. Since it is unethical to use pictures of people, we will be using animals to create our model. Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. The classification accuracies of the VGG-19 model will be visualized using the … I got the code for dog/cat image classification and I compiled and ran and got 80% accuracy. The path is where we define the image location and finally the test_single_image cell block will print out the final result, depending on the prediction from the second cell block. Kaggle Notebooks come with popular data science packages like TensorFlow and PyTorch pre-installed in Docker containers (see the Python image GitHub repo) that run on Google Compute Engine VMs. Learning is handy because it comes with pre-made neural networks are the dominant classes in the original VGGNet paper plausible! Histograms as features number of outlying inputs to over-influence the training curve sufficient... Multi-Class image classification using CNN and SVM on a Kaggle data set is below. 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Metrics to give us a neat result Kaggle Consumer Finance Complaints into 11 classes algorithms 1.