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    • Coco dataset paper. This is part of the fast.

  • Coco dataset paper YOLOv8 and the COCO data set are useful in real-world applications and case studies. Nov 5, 2023 · Benchmark datasets are used to profile and compare algorithms across a variety of tasks, ranging from image classification to segmentation, and also play a large role in image pretraining algorithms. High-quality and diversified instruction following data is the key to this fine-tuning process. 5. Section II shows the overview of the COCO dataset This paper describes the COCO-Text dataset. There are 164k images in COCO-stuff dataset that span over 172 categories including 80 things, 91 The current state-of-the-art on COCO 2017 is CP-DETR-L Swin-L(Fine tuning separately in COCO). This paper describes the COCO-Text dataset. 5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. The COCO-Text dataset contains non-text images, legible text images and illegible text images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. In this paper, we discover and annotate visual attributes for the COCO dataset. 40,000 images 14 tasks Dataset; Download Nov 21, 2018 · We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. Visual instruction fine-tuning (IFT) is a vital process for aligning MLLMs' output with user's intentions. There are 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands) annotations for each person in the image. Datasets havespurredthe advancement of numer-ous fields in computer vision. COCO-WholeBody is an extension of COCO dataset with whole-body annotations. It is based on the MS COCO dataset, which contains images of complex everyday scenes. The rest of this paper is organized as follows. However, these methods often struggle with domain-specific entities and long-tail concepts absent from their training data, particularly in identifying specific individuals. info@cocodataset. Jan 23, 2023 · COCO データセット (COCO dataset, Common Objects in COntext) は，コンテキストあり(In Context)の状況で画像から物体中心の(Object-centric)の画像認識タスク各種をおこなう目的で用いる，大規模画像データセットである [Lin et al. 1007/978-3-319-10602-1_48) We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. (MS COCO) dataset while allowing the users to use their externally The current state-of-the-art on MS COCO is DEIM-D-FINE-X+. COCO is a large-scale object detection, segmentation, and captioning dataset. For the training and validation images, five independent human generated captions will be provided. COCONut harmonizes segmentation annotations across semantic, instance, and panoptic segmentation with meticulously crafted high-quality masks, and The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. Emphasis is placed on results with little regard to the actual content within the dataset. Mar 9, 2025 · COCO128 Dataset Introduction. The dataset consists of 328K images. Following the layout of the COCO dataset, each instance is assigned random color information, and Aug 1, 2019 · This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. Color Histogram Contouring: A New Training-Less Approach to Object Detection Article The current state-of-the-art on COCO test-dev is Co-DETR. The RefCOCO dataset is a referring expression generation (REG) dataset used for tasks related to understanding natural language expressions that refer to specific objects in images. However, the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. COCO-Tasks Dataset. This is part of the fast. The dataset is based on the MS COCO dataset, which contains Occluded COCO is automatically generated subset of COCO val dataset, collecting partially occluded objects for a large variety of categories in real images in a scalable manner, where target object is partially occluded but the segmentation mask is connected. COCO has several features: Paper (ECCV2014): link. Some notable datasets include the Middlebury datasetsforstereovision[20],multi-viewstereo[36]andopticalflow[21]. methods, and datasets. In this game, the first player views an image with a segmented target object and writes Dec 30, 2024 · Recent advancements in deep learning have significantly enhanced content-based retrieval methods, notably through models like CLIP that map images and texts into a shared embedding space. Source: Microsoft COCO Captions: Data Collection and Evaluation Server Sep 10, 2024 · 3D-COCO is a dataset composed of MS COCO images with 3D models aligned on each instance. 3D-COCO is an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. By building the datasets (SDOD, Mini6K, Mini2022 and Mini6KClean) and analyzing the experiments, we demonstrate that data labeling errors (missing labels, category label errors, inappropriate labels) are another factor that affects the detection performance With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. To use COCONut-Large, you need to download the panoptic masks from huggingface and copy the images by the image list from the objects365 image folder. See a full comparison of 24 papers with code. springer. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the Sep 19, 2024 · To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLAC (COCO Occlusion Labels for All Computer Vision Tasks), which is derived from the COCO dataset by manually labelling images into three perceived occlusion levels. Subscribe. The COCO dataset [35], in particular, has played a pivotal role in the development of modern vision models, addressing a wide range of tasks such as object detection [3, 18, 22, 36, 46, 48, 68], segmentation [5–7, 10, 28, 40, 56–58, 64], keypoint detection [20, 24, 45, 54], and image We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. More elaboration about COCO dataset labels can be found in Jan 26, 2016 · This paper describes the COCO-Text dataset. In this paper, validation set 2017 of COCO dataset is evaluated in detail in the most recent YOLOv3 [7] framework, and COCO annotation is discussed. See a full comparison of 36 papers with code. Sep 17, 2016 · In this paper, we discover and annotate visual attributes for the COCO dataset. See a full comparison of 82 papers with code. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. In total the dataset has 2,500,000 labeled instances in 328,000 images. Other vision datasets Datasets have spurred the ad-vancement of numerous fields in computer vision. In this paper, we explore the task Jun 29, 2021 · The COCO dataset has been one of the most popular and influential computer vision datasets since its release in 2014. ,2014]． COCOデータセットのHP. 18M panoptic masks, we introduce COCONut, the COCO Next Universal segmenTation dataset. Notably, the established COCO benchmark has propelled the development of modern detection and segmentation systems. Find papers, benchmarks, and annotations for MS COCO on Papers With Code. COCO contains 330K images, with 200K images having annotations for object detection, segmentation, and captioning tasks. AI and Computer Vision designs famously utilize the COCO dataset for different PC vision SketchyCOCO dataset consists of two parts: Object-level data Object-level data contains $20198(train18869+val1329)$ triplets of {foreground sketch, foreground image, foreground edge map} examples covering Some val scene images come from the train images of the COCO-Stuff dataset for increasing the number of the val images of the SketchyCOCO establishment of comprehensive benchmark datasets. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The current state-of-the-art on COCO test-dev is ViTPose (ViTAE-G, ensemble). Objects are labeled using per-instance segmentations […] May 1, 2014 · With a total of 2. See a full comparison of 225 papers with code. May 1, 2014 · We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. The BerkeleySegmentation Data Set (BSDS500) [37] has been used extensively to COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. In total there are 22184 training images and 7026 validation images with at least one instance of legible text. Apr 30, 2014 · MS COCO [57] The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. 11,752 PAPERS • 96 BENCHMARKS The COCO-Text dataset is a dataset for text detection and recognition. The COCO-MIG benchmark (Common Objects in Context Multi-Instance Generation) is a benchmark used to evaluate the generation capability of generators on text containing multiple attributes of multi-instance objects. Mar 17, 2025 · Key Features. MS COCO is a large-scale dataset for object detection, segmentation, captioning, and other tasks. The dataset is based on the MS COCO dataset, which contains Dec 11, 2023 · The study also discusses YOLOv8 architecture and performance limits and COCO data set biases, data distribution, and annotation quality. Using our COCO Attributes dataset, a fine-tuned classification system can do more The current state-of-the-art on COCO 2014 is VAST. Other Vision Datasets. It is important to question what kind of information is being learned from these datasets and what are COCO-O(ut-of-distribution) contains 6 domains (sketch, cartoon, painting, weather, handmake, tattoo) of COCO objects which are hard to be detected by most existing detectors. COCO dataset is a huge scope object identification dataset distributed by Microsoft. Read previous issues. Some notable datasets include the Middlebury datasets for stereo vision [16], multi-view stereo [32] and optical flow [17]. In search for an arXiv. Objects are labeled using per-instance COCO Captions contains over one and a half million captions describing over 330,000 images. Like every dataset, COCO contains subtle errors and imperfections stemming from its annotation procedure. com. /coconut_datasets" by default, you can change it to your preferred path by adding "--output_dir YOUR_DATA_PATH". With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server 276). 3D-COCO was designed to achieve computer vision tasks such as 3D reconstruction or image detection configurable with textual, 2D image, and 3D CAD model queries. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. May 1, 2014 · COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts. With the advent of high-performing models, we ask whether these errors of COCO are hindering its utility in reliably benchmarking further progress. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recogni-tion. It serves as a popular benchmark dataset for various areas of machine learning . If you use this dataset in your research please cite arXiv:1405. COCO Dataset | Paper Abstract. Discussing the difficulties of generalizing YOLOv8 for diverse object detection tasks. org. 0312 [cs. For the training and validation images, five independent human generated captions are be provided for each image. Ultralytics COCO128 is a small, but versatile object detection dataset composed of the first 128 images of the COCO train 2017 set. ai datasets collection hosted by AWS for convenience of fast. Here are the key details about RefCOCO: Collection Method: The dataset was collected using the ReferitGame, a two-player game. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. This dataset allow models to produce high quality captions for images. We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations. This benchmark consists of 800 sets of examples sampled from the COCO dataset. Read previous issues LAION-COCO is the world’s largest dataset of 600M generated high-quality captions for publicly available web-images. " - kdexd/coco-rem (DOI: 10. The images were not The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. The dataset has a total of 6,782 images and 26,624 labelled bounding boxes. Home; People We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understand-ing. Recent studies propose to construct visual IFT datasets through a multifaceted Mar 22, 2025 · The data will be saved at ". May 1, 2023 · In this paper, we rethink the PASCAL-VOC and MS-COCO dataset for small object detection. The dataset comprises 80 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories -- for example, rendering multi-label classifications such as ''sleeping spotted curled-up cat'' instead of simply ''cat''. Some notable datasets include the Middlebury datasets for stereo vision [20], multi-view stereo [36] and optical flow [21]. Mar 1, 2024 · The Microsoft Common Objects in COntext (MS COCO) dataset contains 91 common object categories with 82 of them having more than 5,000 labeled instances, Fig. The images are extracted from the english subset of Laion-5B with an ensemble of BLIP L/14 and 2 CLIP versions (L/14 and RN50x64). Read previous COCO-Tasks dataset from the CVPR 2019 paper: What Object Should I Use? - Task Driven Object Detection. 6. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting. It consists of: 123287 images 78736 train questions 38948 test questions 4 types of questions: object, number, color, location Answers are all one-word. Each person has annotations for 29 action categories and there are no interaction labels including objects. With the goal of enabling deeper object understand-ing, we deliver the largest attribute dataset to date. Leaderboard (Detection): This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. In our dataset, we ensure that each object category has a significant number of instances, Figure5. libraries, methods, and datasets. COCO-QA is a dataset for visual question answering. The code is documented and designed to be easy to Original COCO paper; COCO dataset release in 2014; COCO dataset release in 2017; Since the labels for COCO datasets released in 2014 and 2017 were the same, they were merged into a single file. our dataset, we ensure that each object category has a significant number of instances, Fig. Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. Read previous issues Jan 26, 2016 · This paper describes the COCO-Text dataset. The model generates bounding boxes and segmentation masks for each instance of an object in the image. 1 code implementation • ICCV 2023 To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. Using COCO-OLAC, we systematically assess and quantify the impact of occlusion on COCO is a large-scale object detection, segmentation, and captioning dataset. May 1, 2014 · The YOLO-v4 model used in this paper was trained using selected images from the COCO dataset [34]. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. CV]. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. I will distinguish the items with the assistance of coco dataset and python. The file name should be self-explanatory in determining the publication type of the labels. With over 120,000 images and these, COCO dataset [8] is popular because it is large-scale, and contains natural scenes. Using our COCO Attributes dataset, a ne-tuned classi cation system can do more than recognize object categories { for example, rendering multi-label Jan 17, 2024 · Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. Code for the paper "Benchmarking Object Detectors with COCO: A New Path Forward. Apr 12, 2024 · By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5. Mar 27, 2024 · The Common Objects in Context (COCO) dataset has been instrumental in benchmarking object detectors over the past decade. Originally equipped with Apr 1, 2015 · In this paper we describe the Microsoft COCO Caption dataset and evaluation server. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. See a full comparison of 47 papers with code. ai students. Oct 7, 2022 · Object recognition comprises of perceiving, recognizing and finding objects with precision. org e-Print archive Apr 12, 2024 · In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. xyojm dpxfd xvttgj owt fskngl nkk onb qpe bze ezwctxa