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Computer Vision : Series

by akshara Updated: May 28, 2022

ğ‚ğŽğŒğğ”ğ“ğ„ğ‘ ğ•ğˆğ’ğˆğŽğ is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models machines can accurately identify and classify objects and then react to what they "see". It is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos.

𝑯𝑰𝑺𝑻𝑶𝑹𝒀 𝑶𝑭 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵: In the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. In the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑾𝑶𝑹𝑲𝑰𝑵𝑮: Acquiring an image Processing the image Understanding the image 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑨𝑺 𝑨 𝑱𝑰𝑮𝑺𝑨𝑾 𝑷𝑼𝒁𝒁𝑳𝑬: Computers assemble visual images like a jigsaw puzzle. All these pieces of the jigsaw are to be assembled into an image and that's how neural networks work for computer vision. They distinguish many different pieces of the image, they identify the edges and then model the subcomponents. Using filtering and a series of actions through deep network layers, they can piece all the parts of the image together. the computer is often fed hundreds or thousands of related images for training and recognizing specific objects. 𝑨𝑷𝑷𝑳𝑰𝑪𝑨𝑻𝑰𝑶𝑵 𝑶𝑭 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵: Computer Vision for Defect Detection Computer Vision for Intruder Detection Computer Vision for Screen Reader Computer Vision for Code and Character Reader (OCR) Computer Vision in robotics for Bin Picking

𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑺𝑬𝑹𝑰𝑬𝑺

ğˆğ¦ğšğ ğž 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧

Image classification is used to categorize whether an object is present in the likelihood of an image. Feature extraction is a part of the dimensionality reduction process, in which, an initial set of the raw data is divided and reduced into more manageable groups. Image classification is the process of categorizing and labeling groups of pixels or vectors within an image based on specific rules.

mmclassificationby open-mmlab

Python star image 1210 Version:v0.22.1

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OpenMMLab Image Classification Toolbox and Benchmark

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mmclassificationby open-mmlab

Python star image 1210 Version:v0.22.1 License: Permissive (Apache-2.0)

OpenMMLab Image Classification Toolbox and Benchmark
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pytorch_image_classificationby hysts

Python star image 858 Version:Current

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PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

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pytorch_image_classificationby hysts

Python star image 858 Version:Current License: Permissive (MIT)

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
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HRNet-Image-Classificationby HRNet

Python star image 672 Version:PretrainedWeights

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Train the HRNet model on ImageNet

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HRNet-Image-Classificationby HRNet

Python star image 672 Version:PretrainedWeights License: Permissive (MIT)

Train the HRNet model on ImageNet
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GoogLeNet-Inceptionby conan7882

Python star image 232 Version:Current

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TensorFlow implementation of GoogLeNet and Inception for image classification.

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GoogLeNet-Inceptionby conan7882

Python star image 232 Version:Current License: Permissive (MIT)

TensorFlow implementation of GoogLeNet and Inception for image classification.
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wildcat.pytorchby durandtibo

Python star image 241 Version:Current

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PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017

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wildcat.pytorchby durandtibo

Python star image 241 Version:Current License: Permissive (MIT)

PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
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Object Detection is used to recognize the object with a bounding box in an image. Object detection is an important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications.

Object-Detection-API-Tensorflowby Stick-To

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Object Detection API Tensorflow

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Object-Detection-API-Tensorflowby Stick-To

Python star image 274 Version:Current License: Permissive (MIT)

Object Detection API Tensorflow
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ssd.pytorchby amdegroot

Python star image 4335 Version:Current

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A PyTorch Implementation of Single Shot MultiBox Detector

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ssd.pytorchby amdegroot

Python star image 4335 Version:Current License: Permissive (MIT)

A PyTorch Implementation of Single Shot MultiBox Detector
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flask-object-detectionby AIZOOTech

Python star image 82 Version:Current

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Frontend and backend separated object detection demo build with Flask, TensorFlow.

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flask-object-detectionby AIZOOTech

Python star image 82 Version:Current License: Permissive (MIT)

Frontend and backend separated object detection demo build with Flask, TensorFlow.
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Object-Detection-APIby theAIGuysCode

Python star image 211 Version:Current

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Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask

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Object-Detection-APIby theAIGuysCode

Python star image 211 Version:Current License: Permissive (Apache-2.0)

Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask
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autogluonby awslabs

Python star image 4341 Version:v0.4.0

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AutoGluon: AutoML for Image, Text, and Tabular Data

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autogluonby awslabs

Python star image 4341 Version:v0.4.0 License: Permissive (Apache-2.0)

AutoGluon: AutoML for Image, Text, and Tabular Data
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Instance Segmentation is identifying each object instance for every known object within an image. Instance segmentation assigns a label to each pixel of the image. Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

PaddleDetectionby PaddlePaddle

Python star image 6559 Version:v2.3.0

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Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

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PaddleDetectionby PaddlePaddle

Python star image 6559 Version:v2.3.0 License: Permissive (Apache-2.0)

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
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maskrcnn-benchmarkby facebookresearch

Python star image 8376 Version:v0.1

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Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

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maskrcnn-benchmarkby facebookresearch

Python star image 8376 Version:v0.1 License: Permissive (MIT)

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
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yolactby dbolya

Python star image 3895 Version:Current

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A simple, fully convolutional model for real-time instance segmentation.

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yolactby dbolya

Python star image 3895 Version:Current License: Permissive (MIT)

A simple, fully convolutional model for real-time instance segmentation.
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yolact_edgeby haotian-liu

Python star image 866 Version:Current

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The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.

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yolact_edgeby haotian-liu

Python star image 866 Version:Current License: Permissive (MIT)

The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
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labelImgby tzutalin

Python star image 15699 Version:v1.8.1

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🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

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labelImgby tzutalin

Python star image 15699 Version:v1.8.1 License: Permissive (MIT)

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images
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