⭐ OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ APOTEK OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ CARA MENGGUGURKAN KANDUNGAN 087776558899 ⭐ PENJUAL OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ TEMPAT OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ LOKASI OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ JUAL OBAT PENGGUGUR KANDUNGAN 087776558899

General Information

Title: COCO-AB Description: The COCO-AB dataset is an extension of the COCO 2014 training set, enriched with additional annotation byproducts (AB). The data includes 82,765 reannotated images from the original COCO 2014 training set. It has relevance in computer vision, specifically in object detection and location. The aim of the dataset is to provide a richer understanding of the images (without extra costs) by recording additional actions and interactions… See the full description on the dataset page: https://huggingface.co/datasets/coallaoh/COCO-AB.

COCO 128 is a subset of 128 images of the larger COCO dataset. It reuses the training set for both validation and testing, with the purpose of proving that your training pipeline is working properly and can overfit this small dataset.

COCO 128 is a great dataset to use the first time you are testing out a new model.

COCO-10 is a database of 5700 color images (with masks) displaying 150 printed color barcodes acquired by different smartphone cameras under different illuminations. The total number of images in COCO-10 is 11700.

COCO-10: COlour BarCOde data-set contains 5700 colour images depicting 150 colour barcodes printed on two different kinds of paper and acquired under different illuminations by different smartphone cameras. Masks specifying the position of the barcodes in the acquired images are also provided. The number 10 in the data-set name refers to the fact that the colours of the barcode lines have been randomly picked up from a palette of 10 colours, including both warm and cold hues. The images of the 150 colour barcodes created from black & white barcodes are released too, along with a set of 150 synthetic colour images, where colour barcodes have been super-imposed over cluttered, real-world backgrounds. The total number of images in COCO-10, including masks, is 11700.

Aim: COCO-10 has been specifically designed for developing and/or testing algorithms for device- and illuminant- invariant colour barcode detection and decoding. It can be used also for developing and testing algorithms for gamut and tone mapping, colour correction, machine colour constancy.

Structure: COCO-10 is organized in three folders: COLOUR-BARCODES (150 images), COLOUR-BARCODES-ON-WHITE-PAPERS (10800 images) and COLOUR-BARCODES-ON-CLUTTERED-BACKGROUND (750 images).

Short Description: Colour barcodes have been generated by coloring the lines of 150 black & white barcodes (see folder COLOUR-BARCODES). These barcode images have been printed on white papers with different density (i.e., 80 gr/m2 and 160 gr/m2) and captured by three smartphone cameras under six illuminants (i.e., a natural light and five artificial lights), yielding 5400 images of colour barcodes on uniform background (see folder COLOUR-BARCODES-ON-HITE-PAPERS, subfolders named CAMERA-X-PAPER-Y, where X and Y denote respectively the camera and the paper density). Barcodes have been also synthetically super-imposed on complex backgrounds, yielding 150 colour images (see folder COLOUR-BARCODES-ON-CLUTTERED-BACKGROUND, subfolder SYNTHETIC-COLOUR-BARCODES-ON-CLUTTERED-BACKGROUND). These latter ware divided into six subgroups, each of which printed on sheets with density either 80 gr/m2 or 160 gr/m2 and acquired by two smartphone cameras under one of the six lights mentioned before, providing 300 images where colour barcodes appear on clutter backgrounds (see folder COLOUR-BARCODES-ON-CLUTTERED-BACKGROUND, subfolders named CAMERA-X-PAPER-Y-LIGHT-Z, where X, Y, Z indicate respectively the camera, the paper density and the light used in that acquisition) . For these acquisitions, six smartphone cameras were used, including those employed for capturing the barcodes printed on white paper. For all the 5700 acquired images we provided masks, i.e., binary images specifying the position of the barcode in the image. Therefore, the total number of images in COCO-10 is 11700.

Microsoft Common Objects in Context (COCO) Dataset

The Common Objects in Context (COCO) dataset is a widely recognized collection designed to spur object detection, segmentation, and captioning research. Created by Microsoft, COCO provides annotations, including object categories, keypoints, and more. The model it a valuable asset for machine learning practitioners and researchers. Today, many model architectures are benchmarked against COCO, which has enabled a standard system by which architectures can be compared.

While COCO is often touted to comprise over 300k images, it's pivotal to understand that this number includes diverse formats like keypoints, among others. Specifically, the labeled dataset for object detection stands at 123,272 images.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

The Roboflow team has worked extensively with COCO. Here are a few links that may be helpful as you get started working with this dataset:

• An introduction to the COCO dataset
• Weird images in COCO, and what that tells us about the utility and limits of COCO

Introduction: Our corpus is an extension of the MS COCO image recognition and captioning dataset. MS COCO comprises images paired with a set of five captions. Yet, it does not include any speech. Therefore, we used Voxygen's text-to-speech system to synthesise the available captions. The addition of speech as a new modality enables MSCOCO to be used for researches in the field of language acquisition, unsupervised term discovery, keyword spotting, or semantic embedding using speech and vision. Our corpus is licensed under a Creative Commons Attribution 4.0 License. Data Set: This corpus contains 616,767 spoken captions from MSCOCO's val2014 and train2014 subsets (respectively 414,113 for train2014 and 202,654 for val2014). We used 8 different voices. 4 of them have a British accent (Paul, Bronwen, Judith, and Elizabeth) and the 4 others have an American accent (Phil, Bruce, Amanda, Jenny). In order to make the captions sound more natural, we used SOX tempo command, enabling us to change the speed without changing the pitch. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. The last third of the captions was kept untouched. We also modified approximately 30% of the original captions and added disfluencies such as "um", "uh", "er" so that the captions would sound more natural. Each WAV file is paired with a JSON file containing various information: timecode of each word in the caption, name of the speaker, name of the WAV file, etc. The JSON files have the following data structure: {"duration": float, "speaker": string, "synthesisedCaption": string, "timecode": list, "speed": float, "wavFilename": string, "captionID": int, "imgID": int, "disfluency": list}. On average, each caption comprises 10.79 tokens, disfluencies included. The WAV files are on average 3.52 seconds long.

This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.

COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.

We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet

This repository contains the following files:

coco_classes_map.txt, contains the mapping for the 80 coco classes

lvis_classes_map.txt, contains the mapping for the 1460 coco classes

openimages_classes_map.txt, contains the mapping for the 601 coco classes

classname_hyperset_definition.csv, contains the final set of 1460 classes, their definition and hierarchy

all-classnames.xlsx, contains a side-by-side view of all classes considered

This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet Zhang et al. 2021, Mask R-CNN He et al. 2017, and a Faster R-CNN+Inception ResNet (trained on the Open Images V4).

This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:

@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }

References:

[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_52

[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. Journal of Imaging, 7(5), p.76.

[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5356-5364).

[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).

[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.

[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.

[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8514-8523).

COCO-WholeBody is an extension of COCO dataset with whole-body annotations. 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.

Dataset Description

This dataset has been converted to COCO format and contains bounding box annotations for content detection.

  Dataset Structure

The dataset is split into training and validation sets:

Training set: 583 images Validation set: 146 images

  Format

The dataset follows the COCO format with the following structure:

images: Contains the image files annotations.json: Contains the COCO format annotations dataset.yaml: Configuration file for training… See the full description on the dataset page: https://huggingface.co/datasets/zigg-ai/content-regions-1k-coco.

This dataset contains all COCO 2017 images and annotations split in training (118287 images) and validation (5000 images).

Pix2Cap-COCO

  Dataset Description

Pix2Cap-COCO is the first pixel-level captioning dataset derived from the panoptic COCO 2017 dataset, designed to provide more precise visual descriptions than traditional region-level captioning datasets. It consists of 20,550 images, partitioned into a training set (18,212 images) and a validation set (2,338 images), mirroring the original COCO split. The dataset includes 167,254 detailed pixel-level captions, each averaging… See the full description on the dataset page: https://huggingface.co/datasets/geshang/Pix2Cap-COCO.

A collection of 3 referring expression datasets based off images in the COCO dataset. A referring expression is a piece of text that describes a unique object in an image. These datasets are collected by asking human raters to disambiguate objects delineated by bounding boxes in the COCO dataset.

RefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are strictly appearance based descriptions, which they enforced by preventing raters from using location based descriptions (e.g., "person to the right" is not a valid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more rich description of objects compared to RefCoco due to differences in the annotation process. In particular, RefCoco was collected in an interactive game-based setting, while RefCocoG was collected in a non-interactive setting. On average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.

Each dataset has different split allocations that are typically all reported in papers. The "testA" and "testB" sets in RefCoco and RefCoco+ contain only people and only non-people respectively. Images are partitioned into the various splits. In the "google" split, objects, not images, are partitioned between the train and non-train splits. This means that the same image can appear in both the train and validation split, but the objects being referred to in the image will be different between the two sets. In contrast, the "unc" and "umd" splits partition images between the train, validation, and test split. In RefCocoG, the "google" split does not have a canonical test set, and the validation set is typically reported in papers as "val*".

Stats for each dataset and split ("refs" is the number of referring expressions, and "images" is the number of images):

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ref_coco', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/ref_coco-refcoco_unc-1.1.0.png" alt="Visualization" width="500px">

COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image.

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.

Annotations: The dataset has annotations for

object detection: bounding boxes and per-instance segmentation masks with 80 object categories, captioning: natural language descriptions of the images (see MS COCO Captions), keypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle), stuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff), panoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road), dense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model. The annotations are publicly available only for training and validation images.

COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting.

Ear acupoint key point detection data set, MS COCO format, divided into training set and test set, and written a sample config configuration file for openMMLab mmPose and mmDet Markers: Zhang Zihao, Tian Wenbo

耳朵穴位关键点检测数据集，MS COCO格式，划分好了训练集和测试集，并写好了样例config配置文件 链接: https://pan.baidu.com/s/1swTLpArj7XEDXW4d0lo7Mg 提取码: 741p 标注人：张子豪、田文博

I share this dataset for the openMMLab 2rd AI Camp.

The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.

Dataset Description

SPRIGHT (SPatially RIGHT) is the first spatially focused, large scale vision-language dataset. It was built by re-captioning ∼6 million images from 4 widely-used datasets:

CC12M Segment Anything COCO Validation LAION Aesthetics

This repository contains the re-captioned data from COCO-Validation Set, while the data from CC12 and Segment Anything is present here. We do not release images from LAION, as the parent images are currently private.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/SPRIGHT-T2I/spright_coco.

Customs records of are available for COCO COLLECTION. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Characteristics of COCO data-set.