!!TOP!! Download Part Of Coco Dataset

From what I personally know, if you're talking about the COCO dataset only, I don't think they have a category for "ships". The closest category they have is "boat". Here's the link to check the available categories:

On my side I had recent difficulties installing fiftyone with Apple Silicon Mac (M1), so I created a script based on pycocotools that allows me to quickly download a subset of the coco 2017 dataset (images and annotations).

Download Part Of Coco Dataset

Download File 🔥 https://shurll.com/2y2Ro4 🔥

I've used posts here on SO and -limit/coco-manager (filter.py file) code to filter COCO dataset to only include annotations and images from classes "person", "car", "bike", "truck" and "bicycle". Now my directory structure is:

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The variability and quality of the data play a crucial role in determining the capabilities and accuracy of machine learning models. Only highly qualitative data will guarantee efficient performance. One of the easiest ways to obtain high-quality data is by using pre-existing, well-established benchmark datasets.

The COCO (Common Objects in Context) dataset is a large-scale image recognition dataset for object detection, segmentation, and captioning tasks. It contains over 330,000 images, each annotated with 80 object categories and 5 captions describing the scene. The COCO dataset is widely used in computer vision research and has been used to train and evaluate many state-of-the-art object detection and segmentation models.

Class imbalance happens when the number of samples in one class significantly differs from other classes. In the COCO dataset context, some objects' classes have many more image instances than others.

Additionally, bias in the dataset can lead to the model overfit on the majority class, which means that it will perform well in this class but poorly in other classes. Several techniques can be used to mitigate the class imbalance issue, such as oversampling, undersampling, and synthetic data generation.

The COCO dataset can be used to train object detection models. The dataset provides bounding box coordinates for 80 different types of objects, which can be used to train models to detect bounding boxes and classify objects in the images.

To train a semantic segmentation model, we need a dataset that contains images with corresponding pixel-level annotations for each class in the image. These annotations are typically provided in the form of masks, where each pixel is assigned a label indicating the class to which it belongs.

Once a dataset is available, a deep learning model such as a Fully Convolutional Network (FCN), U-Net, or Mask-RCNN can be trained. These models are designed to take an image as input and produce a segmentation mask as output. After training, the model can segment new images and provide accurate and detailed annotations.

The COCO dataset includes keypoint annotations for over 250,000 people in more than 200,000 images. These annotations provide the x and y coordinates of 17 keypoints on the body, such as the right elbow, left knee, and right ankle.

Researchers and practitioners can train a deep learning model such as Multi-Person Pose Estimation (MPPE) or OpenPose on the COCO dataset. These models are designed to take an image as input and produce a set of keypoints as output.

The COCO dataset also includes evaluation metrics for panoptic segmentation, such as PQ (panoptic quality) and SQ (stuff quality), which are used to measure the performance of models trained on the dataset.

The dense pose is a computer vision task that estimates the 3D pose of objects or people in an image. It is a challenging task as it requires not only detecting the objects but also estimating the position and orientation of each part of the object, such as the head, arms, legs, etc.

In the context of the COCO dataset, dense pose refers to the annotations provided in the dataset that map pixels in images of people to a 3D model of the human body. These annotations are provided for over 39,000 photos in the dataset and feature over 56,000 tagged persons. Each person is given an instance ID, a mapping between pixels indicating that person's body, and a template 3D model.

To use the dense pose information from the COCO dataset, researchers can train a deep learning model such as DensePose-RCNN on the dataset. The dense pose estimate includes the 3D position and orientation of each part of the human body in the image.

MS COCO (Microsoft Common Objects in Context) is a large-scale image dataset containing 328,000 images of everyday objects and humans. The dataset contains annotations you can use to train machine learning models to recognize, label, and describe objects.

The MS COCO dataset is maintained by a team of contributors, and sponsored by Microsoft, Facebook, and other organizations. It is provided under a Creative Commons Attribution 4.0 License. According to this license you are allowed to:

The annotations section provides a list of all object annotations for every image in the dataset. The iscrowd field indicates if there are several objects of the same type included in the annotation. The bbox field provides the coordinates of the bounding box. The category_id field indicates to which category this object was classified.

Multiple studies have been conducted on biases in image datasets, including the MS COCO dataset which is very widely used in computer vision models. In particular, research by [A, B, and C] showed that the MS COCO dataset has several significant biases:

The researchers explain that these biases make their way into datasets like MS COCO through the initial selection of images, manual captions written by humans (who have their own biases), and automated captions, which are inspired by the manual captions.

While some of these biases stem from the way datasets like MS COCO were assembled, some of them are a result of real-life disparities. For example, if a certain combination of race and gender is a small part of the population, naturally, only a few images of that type of person will appear in the dataset, making it difficult for computer vision algorithms to recognize them.

The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.

Splits:The first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.

Based on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.

The computer vision research community benchmarks new models and enhancements to existing models to test model performance. Benchmarking happens using standard datasets which can be used across models. With this approach, the efficacy of various models can be compared, in general, to show how one model is more or less performant than another.

Common Objects in Context (COCO) is one such example of a benchmarking dataset, used widely throughout the computer vision research community. It even has applications for general practitioners in the field, too.

The Microsoft Common Objects in Context (COCO) dataset is the gold standard benchmark for evaluating the performance of state of the art computer vision models. COCO contains over 330,000 images, of which more than 200,000 are labelled, across dozens of categories of objects. COCO is a collaborative project maintained by computer vision professionals from numerous prestigious institutions, including Google, Caltech, and Georgia Tech.

The COCO dataset contains images from over 80 "object" and 91 generic "stuff" categories, which means the dataset can be used for benchmarking general-purpose models more effectively than small-scale datasets.

Of course, no computer vision model is perfect by all metrics. The COCO dataset provides a benchmark for evaluating the periodic improvement of these models through computer vision research. Practitioners and researchers can benchmark models to see how they have evolved with changes, allowing the community to chart the growth of specific models over time. Entirely different models can be benchmarked on COCO, too.

The COCO dataset also provides a base dataset to train computer vision models in a supervised training method. Once the model is trained on the COCO dataset, it can be fine-tuned to learn other tasks, with a custom dataset. Thus, you can think of COCO like a springboard: it will help you build a generic model, and you can customize it with your own data to improve performance for specific tasks.

In the video below, we discuss how to get started with transfer learning from the COCO dataset. This video dives deep into what objects are in the COCO dataset and how well different objects are represented.

The COCO dataset can be used for multiple computer vision tasks. COCO is commonly used for object detection, semantic segmentation, and keypoint detection. Let's discuss each of these problem types in more detail.

In the COCO dataset class list, we can see that the COCO dataset is heavily biased towards major class categories - such as person, and lightly populated with minor class categories - such as toaster. There are many classes of objects that are annotated hundreds of times in the dataset, from dogs to skateboards to laptops. Some objects are not well represented, such as toasters. There are only 9 annotations corresponding to toasters in the dataset. ff782bc1db