roads

MJ-COCO-2025 is a modified version of the MS-COCO-2017 dataset, in which the annotation errors have been automatically corrected using model-driven methods. The name "MJ" originates from the initials of Min Je Kim, the individual who updated the dataset. "MJ" also stands for "Modification & Justification," emphasizing that the modifications were not manually edited but were systematically validated through machine learning models to increase reliability and quality. Thus, MJ-COCO-2025 reflects both a personal identity and a commitment to improving the dataset through thoughtful modification, ensuring improved accuracy, reliability and consistency. The comparative results of MS-COCO and MJ-COCO datasets are presented in Table 1 and Figure 1. The MJ-COCO-2025 dataset features the improvements, including fixes for group annotations, addition of missing annotations, removal of redundant or overlapping labels, etc. These refinements aim to improve training and evaluation performance in object detection tasks.

Summary of Improvements:

The re-labeled MJ-COCO-2025 dataset demonstrates substantial improvements in annotation quality, with significant increases in several categories and minor corrections in a few due to previous over-annotations or misclassifications, as shown in Table 1 when compared to the original MS-COCO-2017 dataset.

Table 1: Comparison of Class-wise Annotations: MS-COCO-2017 and MJ-COCO-2025. Class Names | MS-COCO | MJ-COCO | Difference | Class Names | MS-COCO | MJ-COCO | Difference ---------------------|---------|---------|------------|----------------------|---------|---------|------------ Airplane | 5,135 | 5,810 | 675 | Kite | 9,076 | 15,092 | 6,016 Apple | 5,851 | 19,527 | 13,676 | Knife | 7,770 | 6,697 | -1,073 Backpack | 8,720 | 10,029 | 1,309 | Laptop | 4,970 | 5,280 | 310 Banana | 9,458 | 49,705 | 40,247 | Microwave | 1,673 | 1,755 | 82 Baseball Bat | 3,276 | 3,517 | 241 | Motorcycle | 8,725 | 10,045 | 1,320 Baseball Glove | 3,747 | 3,440 | -307 | Mouse | 2,262 | 2,377 | 115 Bear | 1,294 | 1,311 | 17 | Orange | 6,399 | 18,416 | 12,017 Bed | 4,192 | 4,177 | -15 | Oven | 3,334 | 4,310 | 976 Bench | 9,838 | 9,784 | -54 | Parking Meter | 1,285 | 1,355 | 70 Bicycle | 7,113 | 7,853 | 740 | Person | 262,465 | 435,252 | 172,787 Bird | 10,806 | 13,346 | 2,540 | Pizza | 5,821 | 6,049 | 228 Boat | 10,759 | 13,386 | 2,627 | Potted Plant | 8,652 | 11,252 | 2,600 Book | 24,715 | 35,712 | 10,997 | Refrigerator | 2,637 | 2,728 | 91 Bottle | 24,342 | 32,455 | 8,113 | Remote | 5,703 | 5,428 | -275 Bowl | 14,358 | 13,591 | -767 | Sandwich | 4,373 | 3,925 | -448 Broccoli | 7,308 | 14,275 | 6,967 | Scissors | 1,481 | 1,558 | 77 Bus | 6,069 | 7,132 | 1,063 | Sheep | 9,509 | 12,813 | 3,304 Cake | 6,353 | 8,968 | 2,615 | Sink | 5,610 | 5,969 | 359 Car | 43,867 | 51,662 | 7,795 | Skateboard | 5,543 | 5,761 | 218 Carrot | 7,852 | 15,411 | 7,559 | Skis | 6,646 | 8,945 | 2,299 Cat | 4,768 | 4,895 | 127 | Snowboard | 2,685 | 2,565 | -120 Cell Phone | 6,434 | 6,642 | 208 | Spoon | 6,165 | 6,156 | -9 Chair | 38,491 | 56,750 | 18,259 | Sports Ball | 6,347 | 6,060 | -287 Clock | 6,334 | 7,618 | 1,284 | Stop Sign | 1,983 | 2,684 | 701 Couch | 5,779 | 5,598 | -181 | Suitcase | 6,192 | 7,447 | 1,255 Cow | 8,147 | 8,990 | 843 | Surfboard | 6,126 | 6,175 | 49 Cup | 20,650 | 22,545 | 1,895 | Teddy Bear | 4,793 | 6,432 | 1,639 Dining Table | 15,714 | 16,569 | 855 | Tennis Racket | 4,812 | 4,932 | 120 Dog | 5,508 | 5,870 | 362 | Tie | 6,496 | 6,048 | -448 Donut | 7,179 | 11,622 | 4,443 | Toaster | 225 | 320 | 95 Elephant | 5,513 | 6,233 | 720 | Toilet | 4,157 | 4,433 | 276 Fire Hydrant ...

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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.

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Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

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  Data Splits

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  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/zhumingwu/coco.

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

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.

Custom Training with YOLOv7 🔥

Some Important links

• Model Inference🤖
• 🚀Training Yolov7 on Kaggle
• Weight and Biases 🐝
• HuggingFace 🤗 Model Repo

Contact Information

• Name - Owais Ahmad
• Phone - +91-9515884381
• Email - owaiskhan9654@gmail.com
• Portfolio - https://owaiskhan9654.github.io/

Objective

To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.

Data Acquisition

The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.

• Link to the Downloadable Dataset

from IPython.display import Markdown, display

display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))

Custom Training with YOLOv7 🔥

In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:

• Export the dataset to YOLOv7
• Train YOLOv7 to recognize the objects in our dataset
• Evaluate our YOLOv7 model's performance
• Run test inference to view performance of YOLOv7 model at work

📦 YOLOv7

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/car-person-2.PNG" width=800>

Image Credit - jinfagang

Step 1: Install Requirements

!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow

Downloading YOLOV7 starting checkpoint

!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"

import os
import glob
import wandb
import torch
from roboflow import Roboflow
from kaggle_secrets import UserSecretsClient
from IPython.display import Image, clear_output, display # to display images



print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">

I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!

YOLOv7-Car-Person-Custom

try:
  user_secrets = UserSecretsClient()
  wandb_api_key = user_secrets.get_secret("wandb_api")
  wandb.login(key=wandb_api_key)
  anonymous = None
except:
  wandb.login(anonymous='must')
  print('To use your W&B account,
Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. 
Get your W&B access token from here: https://wandb.ai/authorize')
  
  
  
wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")

Step 2: Assemble Our Dataset

https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">

In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.

In Roboflow, We can choose between two paths:

• Convert an existing Coco dataset to YOLOv7 format. In Roboflow it supports over 30 formats object detection formats for conversion.
• Uploading only these raw images and annotate them in Roboflow with Roboflow Annotate.

Version v2 Aug 12, 2022 Looks like this.

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">

user_secrets = UserSecretsClient()
roboflow_api_key = user_secrets.get_secret("roboflow_api")

rf = Roboflow(api_key=roboflow_api_key)
project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
dataset = project.version(2).download("yolov7")

Step 3: Training Custom pretrained YOLOv7 model

Here, I am able to pass a number of arguments: - img: define input image size - batch: determine

Please note: this archive requires support for dangling symlinks, which excludes the Windows operating system.

To use this dataset, you will need to download the MS COCO 2017 detection images and expand them to a folder called coco17 in the train_val_combined directory. The download can be found here: https://cocodataset.org/#download You will also need to download the AI2D image description dataset and expand them to a folder called ai2d in the train_val_combined directory. The download can be found here: https://prior.allenai.org/projects/diagram-understanding

License Notes for Train and Val: Since the images in this dataset come from different sources, they are bound by different licenses.

Images for bar charts, x-y plots, maps, pie charts, tables, and technical drawings were downloaded directly from wikimedia commons. License and authorship information is stored independently for each image in these categories in the wikimedia_commons_licenses.csv file. Each row (note: some rows are multi-line) is formatted so:

Images in the slides category were taken from presentations which were downloaded from Wikimedia Commons. The names of the presentations on Wikimedia Commons omits the trailing underscore, number, and file extension, and ends with .pdf instead. The source materials' licenses are shown in source_slices_licenses.csv.

Wikimedia commons photos' information page can be found at "https://commons.wikimedia.org/wiki/File:

License Notes for Testing: The testing images have been uploaded to SlideWiki by SlideWiki users. The image authorship and copyright information is available in authors.csv.

Further information can be found for each image using the SlideWiki file service. Documentation is available at https://fileservice.slidewiki.org/documentation#/ and in particular: metadata is available at "https://fileservice.slidewiki.org/metadata/

This is the SlideImages dataset, which has been assembled for the SlideImages paper. If you find the dataset useful, please cite our paper: https://doi.org/10.1007/978-3-030-45442-5_36

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Mechanical Parts Dataset

The dataset consists of a total of 2250 images obtained by downloading from various internet platforms. Among the images in the dataset, there are 714 images with bearings, 632 images with bolts, 616 images with gears and 586 images with nuts. A total of 10597 manual labeling processes were carried out in the dataset, including 2099 labels belonging to the bearing class, 2734 labels belonging to the bolt class, 2662 labels belonging to the gear class and 3102 labels belonging to the nut class.

Folder Content

The created dataset is divided into 3 as 80% train, 10% validation and 10% test. In the "Mechanical Parts Dataset" folder, there are three separate folders as "train", "test" and "val". In each of these three folders there are folders named "images" and "labels". Images are kept in the "images" folder and tag information is kept in the "labels" folder.

Finally, inside the folder there is a yaml file named "mech_parts_data" for the Yolo algorithm. This file contains the number of classes and class names.

Images and Labels

The dataset was prepared in accordance with the Yolov5 algorithm.
For example, the tag information of the image named "2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg" is stored in the txt file with the same name. The tag information (coordinates) in the txt file are as follows: "class x_center y_center width height".

Update 05.01.2023

***Pascal voc and coco json formats have been added.***

Related paper: doi.org/10.5281/zenodo.7496767

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Credit report of Coco Company Limited contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Credit report of Coco Wolf Ltd contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

With Cooper et al. (2025), we release the stellar particle data for approximately 6400 central galaxies in the Coco simulation (Hellwing et al. 2016) with virial mass M_vir > 10^10 M⊙. This model is based on the STINGS particle tagging technique combined with the semi-analytic model of Lacey et al. (2016).

For the data model of these files, related python scripts and other details, please see https://github.com/nthu-ga/stings-data.

The data are provided in HDF5 format. Each file contains data for a "cutout" around one dark matter halo, including all its satellites.

The files are organized in the following directory structure:

/dr1/cutouts/lacey16/3pc/0153/NNNN

where NNNN is a zero-padded integer ranging (in this release) from 0000 to 0099. This corresponds to the colum IDIR in the galaxies.fits table:

/dr1/cutouts/lacey16/3pc/0153/galaxies.fits

For distribution via Zenodo, data for several IDIRs have been grouped into tarballs. Each .tar.gz file contains data for 5 IDIRs. These files unpack to the directory structure described above. For example, the 0020_0024.tar.gz file unpacks to /dr1/cutouts/lacey16/3pc/0153/[0020,0021,0022,0023,0024].

Data file names are of the form subhalo_{SUBHALOINDEX}_153.hdf5 where {SUBHALOINDEX} is the value of SUBHALOINDEX in galaxies.fits.

The path to the particle data for to a given row in galaxies.fits can therefore be constructed from the IDIR and SUBHALOINDEX columns, as follows:

This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake, if present.

Source data

Individual products used to generate annotations are shown in the following table:

Location Product name

Archipelago sea S2A_MSIL1C_20220515T100031_N0400_R122_T34VEM_20220515T120450

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419

S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233

Gulf of Finland S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944

S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321

S2B_MSIL1C_20220703T094039_N0400_R036_T35VLG_20220703T103953

S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325

Bothnian Bay S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958

S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613

S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748

Bothnian Sea S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEN_20220619T104419

S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233

Kvarken S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008

S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613

S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136

Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.

The raw products can be acquired from Copernicus Data Space Ecosystem.

Annotations

The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:

T34VEM

|-20220515

|-20220619

|-20220721

|-20220813

All layers have a column id, which has the value boat for all annotations.

CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.

As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.

Annotation process The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts.

Product name Number of annotations

S2A_MSIL1C_20220515T100031_N0400_R122_T34VEM_20220515T120450 183

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419 519

S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325 1518

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233 1371

S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944 277

S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321 1205

S2B_MSIL1C_20220703T094039_N0400_R036_T35VLG_20220703T103953 746

S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325 971

S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958 122

S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613 162

S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748 98

S2B_MSIL1C_20210714T100029_N0301_R122_T34VEN_20210714T121056 450

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEN_20220619T104419 66

S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211 424

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233 399

S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008 83

S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613 184

S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136 88

Annotation statistics Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) or 100 (area).

mean min 25% 50% 75% max

Area (m²) 5305.7 567.9 1629.9 2328.2 5176.3 414795.7

Diameter (m) 92.5 33.9 57.9 69.4 108.3 913.9

As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.

Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.

Train-test-split

We used tiles 34VEN and 34VER as the test dataset. For validation, we split the other three tile areas into 5x5 equal sized grid, and used 20 % of the area (i.e 5 cells) for the validation. The same split also makes it possible to do cross-validation.

Post-processing

Before evaluating, the predictions for the test set are cleaned using the following steps:

1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers jarvi (Lakes), meri (Sea) and virtavesialue (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.

2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer vesikivikko (Water rock areas) from the Topographical database.

3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes 38511, 38512, 38513 from the layer vesikivi in the Topographical database.

4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, ty_njr class ids are 1, 2, 3, 4, 5, 8

5. All predictions that are wind turbines, found in Topographical database layer tuulivoimalat

6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.

Model checkpoint for the best performing model is available on Hugging Face platform: https://huggingface.co/mayrajeo/marine-vessel-detection-yolo

Usage The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use geo2ml. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets.

To generate COCO format dataset run

from geo2ml.scripts.data import create_coco_dataset raster_path = '' outpath = '' poly_path = '' layer = '' create_coco_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, dataset_name='', gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)

To generate YOLO format dataset run

from geo2ml.scripts.data import create_yolo_dataset raster_path = '' outpath = '' poly_path = '' layer = '' create_yolo_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)

Credit report of Life Of Coco contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.