Hasty

SearchK Computer Vision Wiki User Docs API Reference Getting started Introduction to the Computer Vision Wiki Overview of topics General best practices Key principles of Computer Vision Convolutional layer Advanced convolutional layers Pooling Overfitting Underfitting Overfitting Vs. Underfitting Downsampling and Upsampling in Machine Learning Computer Vision tasks The complete glossary of the modern Computer Vision tasks Image Classification Object Detection Semantic Segmentation Instance Segmentation Panoptic Segmentation Attribute Prediction Computer Vision model architectures ResNet Faster R-CNN Mask R-CNN DeepLabv3+ U-Net FBNetV3 U-Net++ Efficient Net PAN PSPNet LinkNet FPN RetinaNet Cascade R-CNN FBNetV3IS FBNetV3OD CascadeMask R-CNN HybridTask Cascade Computer Vision metrics Confusion Matrix Intersection over Union (IoU) Accuracy score Hamming score Precision score Recall score Precision-Recall curve F-score Average Precision mean Average Precision (mAP) Loss functions in Machine Learning Comprehensive overview of loss functions in Machine Learning Cross-Entropy Loss Binary Cross-Entropy Loss Focal loss Bounding Box Regression Loss CrossEntropyIoULoss2D Average Loss Solver / Optimizer Comprehensive overview of solvers/optimizers in Deep Learning Adam AMSgrad Variant (Adam) SGD Adadelta Adagrad AdaMax Adamw ASGD Rprop RMSprop Lion Weight Decay Base Learning Rate Momentum (SGD) Dampening (SGD) Epsilon Coefficient Training Parameters Patience Min delta Seed Everything you need to know about batches in Machine Learning Batch Size Iterations Epoch Scheduler Comprehensive overview of learning rate schedulers in Machine Learning ExponentialLR CyclicLR StepLR MultiStepLR ReduceLROnPlateau CosineAnnealingLR Computer Vision augmentations Comprehensive overview of data augmentations in Machine Learning Horizontal Flip Vertical Flip Random Crop Random Sized Crop Rotate Resize Blur Smallest max size Center Crop Color Jitter Gaussian Noise Shift Scale Rotate Longest max size Equalize To gray Shear Mosaic Copy Paste Extrapolation methods Interpolation methods Deployment Primitive deployment using web frameworks Commonly used web frameworks Containerized Deployment Orchestrated Deployment Challenges of Deployment Splits Dataset Split in Machine Learning Computer Vision Wiki Getting started Introduction to the Computer Vision Wiki Introduction to the Computer Vision Wiki Introduction CloudFactory's IT team has been creating internal documentation throughout the years. This documentation condenses the most relevant terms, tips, tricks, and techniques related to Computer Vision (CV) and Vision AI. This wealth of knowledge is the foundation for building the Computer Vision Wiki we use today.Data scientists built this robust Wiki by revising, structuring, enhancing, and compiling comprehensive materials to solidify their understanding of the field. What is the Computer Vision Wiki about? CloudFactory's Computer Vision Wiki offers a comprehensive exploration of this exciting field, a subdomain of Machine Learning (ML). We understand the entire ML lifecycle at CloudFactory, and within that, we specialize in a variety of CV tasks trusted by our clients:Image Classification;Tagging;Object Detection;Instance Segmentation;Semantic Segmentation;Panoptic Segmentation;Attribute Prediction.The Computer Vision Wiki goes beyond theory, focusing on the practical application of key concepts within core tasks like Image Classification, Object Detection, and more.  We aim to equip you with the knowledge you need to implement these concepts in your projects.All terms contain a description, including a brief explanation, some context on applying the concept in practice, links for further theoretical understanding, and, if applicable, a code example for the implementation.This combination of explanations, practical contexts, and code makes this Wiki a valuable resource for anyone who wants to apply Computer Vision to their work.Check the Computer Vision Wiki overview to see what topics are already covered. Who is this Wiki for? The CloudFactory Computer Vision Wiki will be helpful for:Newbies who are at the very basic level in CV and looking for in-depth explanations and valuable resources for terms, concepts, and code they stumble across during their work;Experts who want to refresh their knowledge on a specific topic or look to connect theory with real-life use cases.Teams that discuss the same concepts but use different terms and are looking for ground truth for their terminology use.While the Wiki offers a comprehensive overview, it assumes some prior knowledge of Computer Vision. For beginners, we recommend starting with the introductory CV lecture series by Joseph Redmon. Once you have grasped the fundamentals through the lectures, the Wiki will serve as a valuable resource for deeper exploration. Boost model performance quickly with AI-powered labeling and 100% QA. Learn more Last modified 11mo ago Next - Getting started Overview of topics --- SearchK Computer Vision Wiki User Docs API Reference Getting started Introduction to the Computer Vision Wiki Overview of topics General best practices Key principles of Computer Vision Convolutional layer Advanced convolutional layers Pooling Overfitting Underfitting Overfitting Vs. Underfitting Downsampling and Upsampling in Machine Learning Computer Vision tasks The complete glossary of the modern Computer Vision tasks Image Classification Object Detection Semantic Segmentation Instance Segmentation Panoptic Segmentation Attribute Prediction Computer Vision model architectures ResNet Faster R-CNN Mask R-CNN DeepLabv3+ U-Net FBNetV3 U-Net++ Efficient Net PAN PSPNet LinkNet FPN RetinaNet Cascade R-CNN FBNetV3IS FBNetV3OD CascadeMask R-CNN HybridTask Cascade Computer Vision metrics Confusion Matrix Intersection over Union (IoU) Accuracy score Hamming score Precision score Recall score Precision-Recall curve F-score Average Precision mean Average Precision (mAP) Loss functions in Machine Learning Comprehensive overview of loss functions in Machine Learning Cross-Entropy Loss Binary Cross-Entropy Loss Focal loss Bounding Box Regression Loss CrossEntropyIoULoss2D Average Loss Solver / Optimizer Comprehensive overview of solvers/optimizers in Deep Learning Adam AMSgrad Variant (Adam) SGD Adadelta Adagrad AdaMax Adamw ASGD Rprop RMSprop Lion Weight Decay Base Learning Rate Momentum (SGD) Dampening (SGD) Epsilon Coefficient Training Parameters Patience Min delta Seed Everything you need to know about batches in Machine Learning Batch Size Iterations Epoch Scheduler Comprehensive overview of learning rate schedulers in Machine Learning ExponentialLR CyclicLR StepLR MultiStepLR ReduceLROnPlateau CosineAnnealingLR Computer Vision augmentations Comprehensive overview of data augmentations in Machine Learning Horizontal Flip Vertical Flip Random Crop Random Sized Crop Rotate Resize Blur Smallest max size Center Crop Color Jitter Gaussian Noise Shift Scale Rotate Longest max size Equalize To gray Shear Mosaic Copy Paste Extrapolation methods Interpolation methods Deployment Primitive deployment using web frameworks Commonly used web frameworks Containerized Deployment Orchestrated Deployment Challenges of Deployment Splits Dataset Split in Machine Learning Computer Vision Wiki Getting started Introduction to the Computer Vision Wiki Introduction to the Computer Vision Wiki Introduction CloudFactory's IT team has been creating internal documentation throughout the years. This documentation condenses the most relevant terms, tips, tricks, and techniques related to Computer Vision (CV) and Vision AI. This wealth of knowledge is the foundation for building the Computer Vision Wiki we use today.Data scientists built this robust Wiki by revising, structuring, enhancing, and compiling comprehensive materials to solidify their understanding of the field. What is the Computer Vision Wiki about? CloudFactory's Computer Vision Wiki offers a comprehensive exploration of this exciting field, a subdomain of Machine Learning (ML). We understand the entire ML lifecycle at CloudFactory, and within that, we specialize in a variety of CV tasks trusted by our clients:Image Classification;Tagging;Object Detection;Instance Segmentation;Semantic Segmentation;Panoptic Segmentation;Attribute Prediction.The Computer Vision Wiki goes beyond theory, focusing on the practical application of key concepts within core tasks like Image Classification, Object Detection, and more.  We aim to equip you with the knowledge you need to implement these concepts in your projects.All terms contain a description, including a brief explanation, some context on applying the concept in practice, links for further theoretical understanding, and, if applicable, a code example for the implementation.This combination of explanations, practical contexts, and code makes this Wiki a valuable resource for anyone who wants to apply Computer Vision to their work.Check the Computer Vision Wiki overview to see what topics are already covered. Who is this Wiki for? The CloudFactory Computer Vision Wiki will be helpful for:Newbies who are at the very basic level in CV and looking for in-depth explanations and valuable resources for terms, concepts, and code they stumble across during their work;Experts who want to refresh their knowledge on a specific topic or look to connect theory with real-life use cases.Teams that discuss the same concepts but use different terms and are looking for ground truth for their terminology use.While the Wiki offers a comprehensive overview, it assumes some prior knowledge of Computer Vision. For beginners, we recommend starting with the introductory CV lecture series by Joseph Redmon. Once you have grasped the fundamentals through the lectures, the Wiki will serve as a valuable resource for deeper exploration. Boost model performance quickly with AI-powered labeling and 100% QA. Learn more Last modified 11mo ago Next - Getting started Overview of topics --- SearchK Computer Vision Wiki User Docs API Reference Getting started Introduction to the Computer Vision Wiki Overview of topics General best practices Key principles of Computer Vision Convolutional layer Advanced convolutional layers Pooling Overfitting Underfitting Overfitting Vs. Underfitting Downsampling and Upsampling in Machine Learning Computer Vision tasks The complete glossary of the modern Computer Vision tasks Image Classification Object Detection Semantic Segmentation Instance Segmentation Panoptic Segmentation Attribute Prediction Computer Vision model architectures ResNet Faster R-CNN Mask R-CNN DeepLabv3+ U-Net FBNetV3 U-Net++ Efficient Net PAN PSPNet LinkNet FPN RetinaNet Cascade R-CNN FBNetV3IS FBNetV3OD CascadeMask R-CNN HybridTask Cascade Computer Vision metrics Confusion Matrix Intersection over Union (IoU) Accuracy score Hamming score Precision score Recall score Precision-Recall curve F-score Average Precision mean Average Precision (mAP) Loss functions in Machine Learning Comprehensive overview of loss functions in Machine Learning Cross-Entropy Loss Binary Cross-Entropy Loss Focal loss Bounding Box Regression Loss CrossEntropyIoULoss2D Average Loss Solver / Optimizer Comprehensive overview of solvers/optimizers in Deep Learning Adam AMSgrad Variant (Adam) SGD Adadelta Adagrad AdaMax Adamw ASGD Rprop RMSprop Lion Weight Decay Base Learning Rate Momentum (SGD) Dampening (SGD) Epsilon Coefficient Training Parameters Patience Min delta Seed Everything you need to know about batches in Machine Learning Batch Size Iterations Epoch Scheduler Comprehensive overview of learning rate schedulers in Machine Learning ExponentialLR CyclicLR StepLR MultiStepLR ReduceLROnPlateau CosineAnnealingLR Computer Vision augmentations Comprehensive overview of data augmentations in Machine Learning Horizontal Flip Vertical Flip Random Crop Random Sized Crop Rotate Resize Blur Smallest max size Center Crop Color Jitter Gaussian Noise Shift Scale Rotate Longest max size Equalize To gray Shear Mosaic Copy Paste Extrapolation methods Interpolation methods Deployment Primitive deployment using web frameworks Commonly used web frameworks Containerized Deployment Orchestrated Deployment Challenges of Deployment Splits Dataset Split in Machine Learning Computer Vision Wiki Getting started Introduction to the Computer Vision Wiki Introduction to the Computer Vision Wiki Introduction CloudFactory's IT team has been creating internal documentation throughout the years. This documentation condenses the most relevant terms, tips, tricks, and techniques related to Computer Vision (CV) and Vision AI. This wealth of knowledge is the foundation for building the Computer Vision Wiki we use today.Data scientists built this robust Wiki by revising, structuring, enhancing, and compiling comprehensive materials to solidify their understanding of the field. What is the Computer Vision Wiki about? CloudFactory's Computer Vision Wiki offers a comprehensive exploration of this exciting field, a subdomain of Machine Learning (ML). We understand the entire ML lifecycle at CloudFactory, and within that, we specialize in a variety of CV tasks trusted by our clients:Image Classification;Tagging;Object Detection;Instance Segmentation;Semantic Segmentation;Panoptic Segmentation;Attribute Prediction.The Computer Vision Wiki goes beyond theory, focusing on the practical application of key concepts within core tasks like Image Classification, Object Detection, and more.  We aim to equip you with the knowledge you need to implement these concepts in your projects.All terms contain a description, including a brief explanation, some context on applying the concept in practice, links for further theoretical understanding, and, if applicable, a code example for the implementation.This combination of explanations, practical contexts, and code makes this Wiki a valuable resource for anyone who wants to apply Computer Vision to their work.Check the Computer Vision Wiki overview to see what topics are already covered. Who is this Wiki for? The CloudFactory Computer Vision Wiki will be helpful for:Newbies who are at the very basic level in CV and looking for in-depth explanations and valuable resources for terms, concepts, and code they stumble across during their work;Experts who want to refresh their knowledge on a specific topic or look to connect theory with real-life use cases.Teams that discuss the same concepts but use different terms and are looking for ground truth for their terminology use.While the Wiki offers a comprehensive overview, it assumes some prior knowledge of Computer Vision. For beginners, we recommend starting with the introductory CV lecture series by Joseph Redmon. Once you have grasped the fundamentals through the lectures, the Wiki will serve as a valuable resource for deeper exploration. Boost model performance quickly with AI-powered labeling and 100% QA. Learn more Last modified 11mo ago Next - Getting started Overview of topics --- SearchK Computer Vision Wiki User Docs API Reference Basics Introduction to User Docs Hotkeys shortlist Tutorials Getting started with Hasty How to handle EXIF rotation How to use Hasty API Workspaces Creating a pro workspace Introduction to workspaces Projects Exporting data Project Overview Dashboard AI assistants status overview Creating and editing a project Basics Classes and attributes File manager Taxonomy Upload images Users and Roles Import annotations Automated labeling Advanced options Project Summary Webhooks Mounting buckets Mounting buckets tutorial Project Reports Annotation environment Introduction to the annotation environment When to use which tool Panning and zooming Manual and semi-automated tools Polygon Brush and Eraser Polygon <-> Mask Conversion Merging polygons/masks into one annotation Bounding Box DEXTR ATOM powered by SAM Box to Instance Image Tags Label Attributes Move/edit AI assistants Label Class Prediction Assistant Label Attribute Prediction Assistant Instance Segmentation Assistant Object Detection Assistant Semantic Segmentation Assistant Image Tag Prediction Assistant Quality assurance and control AI Consensus Scoring AI CS in the Annotation Environment AI Consensus Scoring for Image Tags AI Consensus Scoring for Attributes Improving the AI Consensus Scoring results Manual Review Export formats COCO Dataset format Image export Pascal VOC Hasty JSON v1.1 Semantic Segmentation (png images) How to export a project Import formats Import annotations (Beta) Object Detection labels import Instance Segmentation labels import Supported import types Model Playground Accessing Model Playground Data Split Creating a new split Split Results Creating an experiment Individual experiment overview Model Dashboard Dashboard widgets and visualizations Attribute Prediction challenge Image Tagging challenge Instance Segmentation challenge Label class prediction Object Detection challenge Model Exports Deploy Image Transformations TorchScript Sample Inference Scripts Attribute Prediction task Classification task Image Tagging task Instance Segmentation task Object Detection task Semantic Segmentation task Explainability and Saliency maps D-RISE Grad-CAM Active Learning Active Learning in Hasty Margin Variance Entropy How to use Active Learning in Hasty Keypoints How to work with keypoints in Hasty Semi-Supervised Learning Semi-Supervised Learning in Hasty User Docs Basics Introduction to User Docs Introduction to User Docs What is Hasty? Hasty is an image annotation tool for creating ground-truth datasets to be used in Machine Learning applications. Unlike many similar tools, Hasty uses machine learning in the tool itself. This allows you to annotate exponentially quicker than before.sdHasty is focused on the vision AI field bringing such features as AI-assisted annotation, AI-powered Quality Control, and no-code model building solution. When it comes to the exact Computer Vision tasks, here are those that Hasty supports:Classification;Tagging;Object Detection;Instance Segmentation;Semantic Segmentation;Panoptic Segmentation;Attribute Prediction. Getting started To get started with Hasty, go to the Hasty App (https://app.hasty.ai/) and sign up either by using your email or by logging in through your Google account. Signing up/Logging in with your Google account To log in or sign up with your Google account, just click the "Login with Google" button on the first screen. After clicking the button, a new window will open where you can fill in your Google account and password. Do so and you will be logged in (if you have used Hasty before) or signed up (if you haven't used Hasty before). Signing up/Logging in with your email To signup/login with an email address click the "Login with email" button. On the next screen, you will be asked for an email, fill in the email address you want to use and then click on "Continue". When you do so, an email containing a link will be sent to the email you specified. Open up the email, click the link, and then you will be logged in (if you have used Hasty before) or signed up (if you haven't used Hasty before). When you are logged in After you have logged in, you will find yourself in the project overview screen: From here, you can either create a new project, change existing projects, or start testing out our already existing demo project. Do you know that we have a guide for getting you started with Hasty? As the creative masterminds we are, we named it "Getting started with Hasty". Check it out to learn more about how to use Hasty. Working with Hasty We already mentioned that Hasty uses machine learning to speed up the process of annotation. However, our AI tools are not available when you are starting a new project. For these tools to work, we first need you to create enough annotations manually so that we can generate a model.To activate the tools, you will need to completely annotate either ten images (and set their status to "done" or "to review"), or in the case of the Class Predictor Assistant, create 25 annotations.After giving the model(s) some time to train, you will be able to start using our AI tools. Of course, suggestions might not be perfect from the beginning but the models will improve as you annotate more images. In our experience, annotating around 10 annotations per label class is generally a good rule-of-thumb for achieving useful results. Getting help Can’t find what you are looking for in our documentation? Get in contact with us at Hasty by emailing support@hasty.ai if you have any questions or need help. Boost model performance quickly with AI-powered labeling and 100% QA. Learn more Last modified 11mo ago Next - Basics Hotkeys shortlist