Detect_label

## Overview

Detect_label is a dataset for object detection tasks - it contains Tiger Label annotations for 528 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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

Experimental data for the paper "Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data."This dataset supports the study "Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data" by providing resources essential for reproducing and validating the research findings.Dataset Contents and Structure:Hierarchical Model Weights: - .pth files containing trained weights for all alpha regularization values used in hierarchical classification models.MaskRCNN-Segmented Objects: - .jpg files representing segmented objects detected by the MaskRCNN model. - Accompanied by maskrcnn-segmented-objects-dataset.parquet, which includes metadata and classifications: - Columns:masked_image: Path to the segmented image file.confidence: Confidence score for the prediction.predicted_species: Predicted species label.species: True species label.MaskRCNN Weights: - Trained MaskRCNN model weights, including hierarchical CNN models integrated with MaskRCNN in the processing pipeline.Pre-Trained Models:.pt files for all object detectors trained on the Esefjorden Marine Vegetation Segmentation Dataset (EMVSD) in YOLO txt format.Segmented Object Outputs: - Segmentation outputs and datasets for the following models: - RT-DETR: - Segmented objects: rtdetr-segmented-objects/ - Dataset: rtdetr-segmented-objects-dataset.parquet - YOLO-SAG: - Segmented objects: yolosag-segmented-objects/ - Dataset: yolosag-segmented-objects-dataset.parquet - YOLOv11: - Segmented objects: yolov11-segmented-objects/ - Dataset: yolov11-segmented-objects-dataset.parquet - YOLOv8: - Segmented objects: yolov8-segmented-objects/ - Dataset: yolov8-segmented-objects-dataset.parquet - YOLOv9: - Segmented objects: yolov9-segmented-objects/ - Dataset: yolov9-segmented-objects-dataset.parquetUsage Instructions:1. Download and extract the dataset.2. Utilize the Python scripts provided in the associated GitHub repository for evaluation and inference: https://github.com/Ci2Lab/FjordVisionReproducibility:The dataset includes pre-trained weights, segmentation outputs, and experimental results to facilitate reproducibility. The .parquet files and segmented object directories follow a standardized format to ensure consistency.Licensing:This dataset is released under the CC-BY 4.0 license, permitting reuse with proper attribution.Related Materials:- GitHub Repository: https://github.com/Ci2Lab/FjordVision