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Image caption generator Kaggle

Preprocess the captions ¶. link. code. 1) Convert the captions into lowercase. 2) Tokenize the captions into different tokens. 3) Remove all the punctuations from the tokens. 4) add start_index and end_index as pointers to tell the model start of the caption and end of the caption. In [7] We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies

VGG16 and LSTM Image Caption Generator Kaggl

Automatically generating the captions for an image. - wikiabhi/image-caption-generator LSTM+ RESNET50 for predicitng Captions based on Image. Useful in Youtube tag generator, Caption Generator etc. - GitHub - Ankuraxz/Image-Caption-Generator: LSTM+ RESNET50 for predicitng Captions based on Image. Useful in Youtube tag generator, Caption Generator etc

Image captioning Kaggl

Caption Generation Kaggl

This is a PyTorch Tutorial to Image Captioning.. This is the first in a series of tutorials I'm writing about implementing cool models on your own with the amazing PyTorch library.. Basic knowledge of PyTorch, convolutional and recurrent neural networks is assumed. If you're new to PyTorch, first read Deep Learning with PyTorch: A 60 Minute Blitz and Learning PyTorch with Examples Dataset: 3k images of hand gun and their label annotation (bounding box coordination) Model was trained on Colab with 900 epochs using Yolov3. Image caption generator: The idea of this project is to create a Deep Learning model that deliver textual description from given photographs The architecture defined in this article is similar to the one described in the paper Show and Tell: A Neural Image Caption Generator Exclusive Interview with 2x Kaggle Master Gilles Vandewiele! Tanishq Gautam. I am a 22 year old Computer Vision Enthusiast. I intend to improve and contribute to current technology and open new avenues in. # List of Projects - [**Web Scraping**:](#web-scraping) - [Web crawler for scraping stock fundamentals](#web-crawler-for-scraping-stock-fundamentals) - [**Machine.

Image Caption->ResNet,TransformerDecoder[PyTorch] Kaggl

  1. 8) Image Caption Generator. You saw an image and your brain can easily tell what the image is about, but can a computer tell what the image is representing? With the advancement in Deep learning techniques, availability of huge datasets and computer power, we can build models that can generate captions for an image
  2. Upload an image to customize your repository's social media preview. Images should be at least 640×320px (1280×640px for best display)
  3. An RNN is a type of neural network that can work with sequences such as text, sound, videos, finance data, and more. Combining CNNs and RNNs helps us work with images and sequences of words in this case. The goal, then, is to generate captions for a given image. For example, we could run the desired network on Conor McGregor's UFC image.
  4. Given an image like the example below, your goal is to generate a caption such as a surfer riding on a wave. Image Source; License: Public Domain. To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption

Image Captions Generator : Image Caption Generator or Photo Descriptions is one of the Applications of Deep Learning. In Which we have to pass the image to the model and the model does some. This NIC(Neural Image Caption) based approaches produced state-of-art results when it's performance was test on different datasets. It made way for neural networks to explore more in this literature. Datasets. For this project we have some nice datasets like. Flickr_8k (containing 8k images with captions) can be downloaded from Kaggle Caption on bottom side of image Explore and run machine learning code with Kaggle Notebooks | Using data from Flicker8k_Dataset Neural image caption models are trained to maximize the likelihood of producing a caption given an input image, and can be used to generate novel image descriptions

Image Caption Bot. Implementation of 'merge' architecture for generating image captions from paper What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator? using Keras. App is deployed using gRPC and tf-serving on docker. This is a Kaggle competition on Image Segmentation. We have to identify pixels in seismic. Image Caption Generator or Photo Descriptions is one of the Applications of Deep Learning. In Which we have to pass the image to the model and the model does some processing and generating captions or descriptions as per its training. This prediction is sometimes not that much accurate and generates some meaningless sentences 1 Answer1. The Standard Image Captioning Pipeline is to train the model in a single batch (or mini-batch) i.e. get the features from the CNN Image encoder and then feed that into an RNN decoder (features + Real Captions) to produce output captions for the Image. I'd suggest you go through the paper Show and Tell: A Neural Image Caption Generator

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GitHub - adityajn105/image-caption-bot: Implementation of

Today we introduce Conceptual Captions, a new dataset consisting of ~3.3 million image/caption pairs that are created by automatically extracting and filtering image caption annotations from billions of web pages.Introduced in a paper presented at ACL 2018, Conceptual Captions represents an order of magnitude increase of captioned images over the human-curated MS-COCO dataset Image caption generation works in a similar manner. There are two main architectures of an image captioning model. Show and Tell: A Neural Image Caption Generator by the Google Research team. Captioning the images with proper descriptions automatically has become an interesting and challenging problem. In this paper, we present one joint model AICRL, which is able to conduct the automatic image captioning based on ResNet50 and LSTM with soft attention. AICRL consists of one encoder and one decoder. The encoder adopts ResNet50 based on the convolutional neural network, which creates. Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them in a natural language like English. the data cleaning process is important in this project and that's required a lot of time and effort. so neural networks run perfectly on clean data. Posted by Chris Shallue, Software Engineer, Google Brain Team In 2014, research scientists on the Google Brain team trained a machine learning system to automatically produce captions that accurately describe images.Further development of that system led to its success in the Microsoft COCO 2015 image captioning challenge, a competition to compare the best algorithms for computing accurate.

GitHub - wikiabhi/image-caption-generator: Automatically

Its trained on the MNIST dataset on Kaggle. open_nsfw code for running Model and code for Not Suitable for Work (NSFW) classification using deep neural network Caffe models caption_generator A modular library built on top of Keras and TensorFlow to generate a caption in natural language for any input image. HAR-stacked-residual-bidir-LSTM Dataset used for this project is taken from Flickr8K at Kaggle. This dataset contains 8000 images, each provides 5 captions. Link for the dataset: image_descs = captions[image_id] for des in image_descs: ws = des.split( ) w = How to Develop a Deep Learning Photo Caption Generator from Scratch - Machine Learning Mastery Custom image generator keras. Then calling image dataset from directory main directory labels inferred will return a tf data dataset that yields batches of images from the subdirectories class a and class b together with labels 0 and 1 0 corresponding to class a and 1 corresponding to class b. Customized image generator for keras. Jpeg png bmp gif Image-Caption-Generator - A simple implementation of neural image caption generator #opensourc

Methodology to Solve the Task. The task of image captioning can be divided into two modules logically - one is an image based model - which extracts the features and nuances out of our image, and the other is a language based model - which translates the features and objects given by our image based model to a natural sentence.. For our image based model (viz encoder) - we usually rely. The image accompanied by the news is being described using the automated caption generator tool. The caption and the headline of news are being matched against the actual news text content. The resemblance between them indicates how much an image and headline have to do with the description of the news Image Caption Generator. Implementation of 'merge' architecture for generating image captions from paper What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator? This is a kaggle Challenge. Given a pair of faces we have to determine whether they are related or not. Here I have used a Siamese network over VGG. Create your Own Image Caption Generator using Keras! ArticleVideo Book Overview Understand how image caption generator works using the encoder-decoder Know how to create your own image caption generator using Keras . Advanced Computer Vision Deep Learning Image Image Analysis NLP Python Technique Unstructured Data Dumitru Erhan (2015) Show and tell: A neural image With no authority split, the Flickr30K dataset has 31,783 caption generator. CVPR 1, 2 pictures that we will part into 25,000 preparing pictures, 2. K. Xu (2016) Show, attend and tell: Neural image caption 2000 approval pictures, and 3000 pictures for testing

So, to make our image caption generator model, we will be merging these architectures. It is also called a CNN-RNN model. CNN is used for extracting features from the image. We will use the pre-trained model Xception. LSTM will use the information from CNN to help generate a description of the image. Project File Structure. Downloaded from dataset Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them in a natural language like English. Image Caption Generator with CNN - About the Python based Projec ; Add captions to your video directly for free. No downloads required Introduction. Image captioning is a procedure to generate brief textual descriptions of an image. It is possible for humans to give a description of an image just by looking at it. Humans have world knowledge and are able to identify faces and objects. Machine-generated captions could greatly improve the accessibility of images to blind people.

Image caption generators with minimum validation loss can be compared with each other to select the best caption generator based on the benchmark results. For each of the 4 classes, 6 different metrics are computed. They are accuracy, F1-score, F2-score, precision or positive predictive value (PPV), specificity and sensitivity Absolutely depends on the detection problem, the complexity of the features, and the potential for bias in sampling. 1. Define significant covariates, and collect that information with the image data. 2. Collect enough data. Cover the range of eac..

GitHub - Ankuraxz/Image-Caption-Generator: LSTM+ RESNET50

  1. We can easily import Kaggle datasets in just a few steps: Code: Importing CIFAR 10 dataset!pip install kaggle. Image Caption Generator using Deep Learning on Flickr8K dataset. 25, Aug 20. Visualising ML DataSet Through Seaborn Plots and Matplotlib. 05, Aug 20 ; Kaggle is one of the largest communities of Data Scientists
  2. overview image captioning is the process of generating textual description of an image. it uses both natural-language-processing and computer-vision to generate the captions. deep learning • deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks
  3. The Flickr 8k dataset contains 8000 images and each image is labeled with 5 different captions. The dataset is used to build an image caption generator. 9.1 Data Link: Flickr 8k dataset. 9.2 Machine Learning Project Idea: Build an image caption generator using CNN-RNN model. An image caption generator model is able to analyse features of the.
  4. Caption this Meme. Add Meme Add Image Post Comment. Show More Comments. Flip Settings. memes gifs other. Caption this Meme. Created with the Imgflip Meme Generator. IMAGE DESCRIPTION: Kaggle notebook scoring 99.99% accuracy; Trained on whole dataset . hotkeys: D = random, W = upvote, S = downvote, A = back An image tagged disappointed black.
  5. 10. Image Caption Generator. Image captioning is part of NLP. Computers are far behind humans in understanding the context by seeing an image. Image Captioning generator can automatically generate.
  6. Moreover, download the Flicker 8K text dataset containing the image names and caption. You have to use a lot of python libraries here, such as pandas, TensorFlow, Keras, NumPy, Jupyterlab, Tqdm, Pillow, etc. Make sure all of them are available on your computer. The caption generator model is basically a CNN-RNN model
  7. If it does not input a valid image an exception is created. In each case the bad file name is printed out. At the conclusion a list called bad_list contains the list of bad file paths. Note directories must be name 'test' and 'train'. import os import cv2 bad_list= [] dir=r'c:\'PetImages' subdir_list=os.listdir (dir) # create a list of the sub.

Image Caption Generator using Deep Learning on Flickr8K

  1. In most literature of image caption generation, many researchers view RNN as the generator part of the system. However, there are other ways to use the RNN in the whole system. One method is to use the RNN as an encoder for previously generated word, and in the final stages of the model merge the encoded representation with the image
  2. Image Caption Generator or Photo Descriptions is one of the Applica t ions of Deep Learning. In Which we have to pass the image to the model and the model does some processing and generating captions or descriptions as per its training. So to use directly I will provide the link of my dataset on kaggle ,then you can use GCS path to download.
  3. Add a bunch of captions for the same, and we can use it as a dataset for an image caption generator as well. That's the power of a robust dataset. Well, when we are just starting, we shall be working with some of the small and standard machine learning datasets like the CIFAR-10, MNIS, Iris, etc
  4. 2. Image Dataset of Flickr. Flickr is an image hosting service with millions of users worldwide. This dataset has 30,000 images with different captions. You can use this dataset to create a caption generator for images. This dataset is quite famous for image analysis and image description through text
  5. Using custom generator with zip to train a model in python. I wanted to use a custom generator function to train the model. I am returning 3 different outputs using yield and storing them in separate variables using zip function. I don't if it is directly possible to use the yield values without storing in separate variables, since 2 variables.
  6. Flickr 30k Dataset. The Flickr 30k dataset has over 30,000 images, and each image is labeled with different captions. This dataset is used to build an image caption generator. Parkinson Dataset. Parkinson's is a disease that can cause a nervous system disorder and affects the movement

image-caption · GitHub Topics · GitHu

You may also want to check out all available functions/classes of the module keras.applications.vgg16 , or try the search function . Example 1. Project: Image-Caption-Generator Author: dabasajay File: model.py License: MIT License. 9 votes. def RNNModel(vocab_size, max_len, rnnConfig, model_type): embedding_size = rnnConfig['embedding_size'] if. Introduction: what is EfficientNet. EfficientNet, first introduced in Tan and Le, 2019 is among the most efficient models (i.e. requiring least FLOPS for inference) that reaches State-of-the-Art accuracy on both imagenet and common image classification transfer learning tasks.. The smallest base model is similar to MnasNet, which reached near-SOTA with a significantly smaller model Image Caption Generator Python Project; Breast Cancer Classification Project in Python. Get aware with the terms used in Breast Cancer Classification project in Python. What is Deep Learning? An intensive approach to Machine Learning, Deep Learning is inspired by the workings of the human brain and its biological neural networks. Architectures. Image Caption Generator Python Project; What is Colour Detection? Colour detection is the process of detecting the name of any color. Simple isn't it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into color. Light receptors that are present. NLP Projects Idea #2 Image-Caption Generator. Consider you are given a system and asked to describe it. It sounds like a simple task but for someone with weak eyesight or no eyesight, it would be difficult. And that is why designing a system that can provide a description for images would be a great help to them

Importing Kaggle dataset into google colaboratory. While building a Deep Learning model, the first task is to import datasets online and this task proves to be very hectic sometimes. Now go to your Kaggle account and create new API token from my account section, a kaggle.json file will be downloaded in your PC There are reliable resources already mentioned above. But if you need cUAV/Drone based Hyperspectral Data in ENVI BSQ format (400-1000 NM spectral range, 5 nm FWHM and 800 x scan length) you can contact BharatRohan Airborne Innovations Private Lim.. Advanced Python Projects 16 - Predicting and Forecasting Stock Market Prices. Continuing the series - 'Simple Python Project'. These are simple projects with which beginners can start with. This series will cover beginner python, intermediate and advanced python, machine learning and later deep learning. Comments recommending other to-do python. Exploratory data analysis. EDA is among the first few tasks we perform when we get started on any ML project. As discussed in the section on CRISP-DM, data understanding is an important step to uncover various insights about the data and better understand the business requirements and context.. In this section, we will take up an actual dataset and perform EDA using pandas as our data.

You may also want to check out all available functions/classes of the module keras.models , or try the search function . Example 1. Project: Image-Caption-Generator Author: dabasajay File: model.py License: MIT License. 9 votes. def RNNModel(vocab_size, max_len, rnnConfig, model_type): embedding_size = rnnConfig['embedding_size'] if model_type. The Flickr 30k dataset has over 30,000 images, and each image is labeled with different captions. This dataset is used to build an image caption generator. And this dataset is an upgraded version of Flickr 8k used to build more accurate models. Data Link: Flickr image dataset

Create Your Own Image Caption Generator using Keras

Doing a quick Google search, I found an already compiled Kaggle dataset which has trending videos based on different regions. For this project, I used US videos which have a total of 40,949 records. Not a lot to work with, but we make do. Modelling. To caption the images, I used LSTMs with an encoder-decoder model We create a data generator function to create data when the Network needs rather than providing all beforehand. We are training the model on 2 captions rather than 5. Loading glove embeddings and inserting it into the glove embedding matrix. Creating our model and training it. Define the caption generating function and test it on 10 random images

There are over 5,000+ captions in the Instagram Caption Generator Tool. It's the simplest and fastest way to generator captions Image Caption Generator. A neural network to generate captions for an image using CNN and RNN with BEAM Search. Examples. Image Credits : Towardsdatascience. Table of Contents The model architecture is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. We will use the the MS-COCO dataset, preprocess it and take a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model. I trained the model with 50,000 images For image captioning, we are creating an LSTM based model that is used to predict the sequences of words, called the caption, from the feature vectors obtained from the VGG network. The language. Ở đây mình sử dụng dữ liệu Flickr 8k được tải trực tiếp từ kaggle. Cách tải các bạn có thể xem trong file notebook. Dữ liệu bao gồm một bộ 8000 ảnh và một file captions.txt. Mỗi ảnh sẽ có 5 captions làm nhãn. def data_generator (captions, images, w2i, max_length, batch_size):.

You can select a a meme template from our templates or upload your own image to get started making memes! Add captions and images Once you have selected or uploaded an image you can start adding captions and other images to your meme as well as customise them to your lining Show and Tell: A Neural Image Caption Generator (CVPR 2015) Presenters: Tianlu Wang, Yin Zhang October 5 th . Neural Image Caption (NIC) Main Goal: automatically describe the content of an image using properly formed English sentences Human: A young girl asleep on the sofa cuddling a stuffed bear. NIC: A baby is asleep next to a teddy bear

Xception Model Architecture

Caption — Dog standing in the grass eg. A relevant caption for this image might be Dog standing in the grass or Labrador Retriever standing in the grass. In this article, my goal is to introduce this topic and provide an overview of the techniques and architectures that are commonly used to tackle this problem Image caption generator project abstract. Image Caption Generator Based On Deep Neural Networks Jianhui Chen CPSC 503 CS Department Wenqiang Dong CPSC 503 CS Department Minchen Li CPSC 540 CS Department Abstract In this project, we systematically analyze a deep neural networks based image caption generation method

Image Captioning with Tensorflow

My solution for the Web Traffic Forecasting competition hosted on Kaggle. The training dataset consists of approximately 145k time series. Each of these time series represents a number of daily views of a different Wikipedia article, starting from July 1st, 2015 up until September 10th, 2017 The following are 30 code examples for showing how to use keras.preprocessing.image.load_img().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example

caption-generation · GitHub Topics · GitHu

I have no resource to train the training datasets for Show and Tell: A Neural Image Caption Generator, so where to get the pre-trained models? tensorflow pre-trained-model. Share. Follow asked May 26 '17 at 14:30. JustinGong JustinGong. 119 3 3 silver badges 12 12 bronze badges Building powerful image classification models , fit_generator for training Keras a model using Python data generators; ImageDataGenerator for real-time data augmentation; layer freezing and We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. We use the image_dataset_from_directory utility to generate the. Reverse Image Caption • A image generator which generates the target image illustrating the input text and then upload it to Kaggle to get the final score. Evaluation 1. Open terminal and move to the folder containing inception_score.py. Otherwise you have to modif Build an image caption generator using CNN-RNN model. An image caption generator model is able to analyse features of the image and generate English like sentence that describes the image info@cocodataset.org. Home; Peopl

As you can see below, if you execute the above code, you will see that our image feature is just a numpy array of shapes - (18432,). image_feature_dictionary[list(image_feature_dictionary. Keys())[0]].shape (18432,) Next, we will develop an LSTM network (RNN) for generating captions for images. LSTM for generating title And this is where the Python-based Image Caption Generator comes into the picture. It can be implemented with the CNN and LSTM (Long short term memory) models. Plus, as far as the dataset is concerned, this project can be worked on with the help of Flickr_8K. This dataset contains files of Flickr 8k.token. These files contain the names of the. 5. Image Caption Generator. Deep Learning Project Idea - Humans can understand an image easily but computers are far behind from humans in understanding the context by seeing an image. However, technology is evolving and various methods have been proposed through which we can automatically generate captions for the image The ImageDataGenerator class is very useful in image classification. There are several ways to use this generator, depending on the method we use, here we will focus on flow_from_directory takes a path to the directory containing images sorted in sub directories and image augmentation parameters. Let's look on an example: We will use a.

The following are 30 code examples for showing how to use keras.layers.RepeatVector().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example Image Caption Generator Python Project What is Traffic Signs Recognition? There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to I use to solve Kaggle problems to increase my knowledge in this domain. Please feel free to ping me, if you want to participate as a team with me in Kaggle competition. Work Experience. Josh Software, Pune, Maharashtra. Image Caption Generator. Trained a model by merging RNN and CNN to generate the activity occuring on the image Facebook images caption generator; Blog post image alt-text generator . 7. Chatbot Project in Python Thankfully, there are datasets available on Kaggle. All we have to do is use these images to train our model so that, when fed with similar images, it can classify them as having a brain tumor or not. Though such models do not completely.

Plant Pathology - Kaggle Competition Fri 08 May 2020 by Aaron Olson / posts. Thoughts for Food: Food Image Caption Generation Thu 10 October 2019 by Gurdit Chahal & Aaron Olson / posts. Project to generate image captions from image - utilizing image classficiation model and NLP model Image Caption Generator Aug 2019 - Sep 2019 Implemented 'merge' architecture for generating image captions from paper What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption. In this case, the target variable is set to 0 so that the model learns that the given image and caption are not aligned. Here Fake Image means an image generated by the Generator, in this case, the target variable is set to 0 so that the Discriminator model can distinguish between real and fake images [Step1] building keywords-caption pairs to generate Train/Valid/Test datasets\ In COCO caption dataset, it has 5 captions per each image. So we sample 3 ~ 5 nouns of 5 captions as keywords based on word frequency. For example, the baseline 1 model can ouput five different captions with respect to the single class name of castle 10. Image Caption Generator with CNN & LSTM. The aim of the project is to build a model that will automatically generate captions of an image. Humans can easily understand an image by looking at them but this is a hard task for computers. It uses image processing concepts and natural language processing to build the image caption generator model

The generator creates new images starting with random latent vectors for form and style and tries to fool the discriminator into thinking the output images are real. Images of the paintings on Kaggle and the source code are released under F.A. Galatolo, M.G.C.A. Cimino, and G.Vaglini, Generating images from caption and vice. Image Caption Generator (Keras, Sequence models) Developed an image captioning LSTM model using 8k images with 5 different captions each by extracting features from VGG16 pre-trained model. Data preparation is required when working with neural network and deep learning models. Increasingly data augmentation is also required on more complex object recognition tasks. In this post you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras The following are 30 code examples for showing how to use keras.preprocessing.image.img_to_array().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example Read writing from Pradeep Ankem on Medium. In Parallel Universe, I would be a Zen Monk. A Man who is Engineer by Trade and Mathematician by Heart. Every day, Pradeep Ankem and thousands of other voices read, write, and share important stories on Medium

This article will demonstrate how to build a Text Generator by building a Recurrent Long Short Term Memory Network. The conceptual procedure of training the network is to first feed the network a mapping of each character present in the text on which the network is training to a unique number. Each character is then hot-encoded into a vector. You may also want to check out all available functions/classes of the module keras.applications.inception_v3 , or try the search function . Example 1. Project: Image-Caption-Generator Author: dabasajay File: model.py License: MIT License. 9 votes. def RNNModel(vocab_size, max_len, rnnConfig, model_type): embedding_size = rnnConfig['embedding. Image caption Generator is a popular research area of Artificial Intelligence that deals with image understanding and a language description for that image. Generating well-formed sentences requires both syntactic and semantic understanding of the language

AWS hosts quite a few public datasets to play/work around with. The first thing you will need is an Active AWS account. Once you have an account, you can download the available public data sets from AWS S3 or in form of EBS snapshots. Look here fo.. Due to a planned power outage, our services will be reduced today (June 15) starting at 8:30am PDT until the work is complete. We apologize for the inconvenience You already know how difficult it can be to find the best dataset for your model if you're working on a Machine Learning (ML) project. You may have been working on your dilemma, describing it as an ML question, but now you don't have any data to t..

This Google QUEST Q&A Labeling Data is in Kaggle Competition Conducted by Google. The aim of the data set is to improving automated understanding of complex question answer content Machine learning becomes engaging when we face various challenges and thus finding suitable datasets relevant to the use case is essential. Its flexibility and size characterize a data set. Flexibility refers to the number of tasks that it support.. Reconstruct Martian stereo images. Similar to the above, but reconstruct stereo images recorded onMars!1 Background subtraction. Zoom's background subtraction algorithm (i.e. the \virtual background feature) often has a lot of artifacts. I bet that you can do better! See [5] for a state-of-the-art background subtraction method