Cookbook Fine Tuning

## Overview

Cookbook Fine Tuning is a dataset for object detection tasks - it contains Cooking Tools annotations for 4,399 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).

DELTA FINE TUNING

## Overview

DELTA FINE TUNING is a dataset for object detection tasks - it contains Objects annotations for 3,589 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).

Fine Tuning

## Overview

Fine Tuning is a dataset for object detection tasks - it contains Animals annotations for 881 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).

Fine Tuning Yolov5

## Overview

Fine Tuning Yolov5 is a dataset for object detection tasks - it contains Vehices annotations for 3,001 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).

Fine Tuning Contraste

## Overview

Fine Tuning Contraste is a dataset for instance segmentation tasks - it contains F11 annotations for 2,880 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).

Nir Fine Tuning

## Overview

Nir Fine Tuning is a dataset for semantic segmentation tasks - it contains Saros annotations for 203 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).

Fine Tuning Yolo Model

## Overview

Fine Tuning Yolo Model is a dataset for object detection tasks - it contains Firearm Age Smoke Cigarette annotations for 1,657 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).

U2_net Fine Tune

## Overview

U2_net Fine Tune is a dataset for instance segmentation tasks - it contains Medicines annotations for 400 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).

Alert For Safety Violation (Fine Tune Model)

## Overview

Alert For Safety Violation (Fine Tune Model) is a dataset for object detection tasks - it contains Helmet Vest Shoes annotations for 972 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).

Tool_2_Fine_Tuning

## Overview

Tool_2_Fine_Tuning is a dataset for instance segmentation tasks - it contains Person Move Tool annotations for 1,275 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).

Receipt_OCR_fine_tuning

## Overview

Receipt_OCR_fine_tuning is a dataset for object detection tasks - it contains Store annotations for 200 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).

Exploring Object Detection Techniques for MSTAR IU Mixed Targets Dataset

Introduction: The rapid advancements in machine learning and computer vision have significantly improved object detection capabilities. In this project, we aim to explore and develop object detection techniques specifically tailored to the MSTAR IU Mixed Targets. This dataset, provided by the Sensor Data Management System, offers a valuable resource for training and evaluating object detection models for synthetic aperture radar (SAR) imagery.

Objective: Our primary objective is to develop an efficient and accurate object detection model that can identify and localize various targets within the MSTAR IU Mixed Targets dataset. By achieving this, we aim to enhance the understanding and applicability of SAR imagery in real-world scenarios, such as surveillance, reconnaissance, and military applications.

Ethics: As responsible researchers, we recognize the importance of ethics in conducting our project. We are committed to ensuring the ethical use of data and adhering to privacy guidelines. The MSTAR IU Mixed Targets dataset provided by the Sensor Data Management System will be used solely for academic and research purposes. Any personal information or sensitive data within the dataset will be handled with utmost care and confidentiality.

Data Attribution and Giving Credit: We deeply appreciate the Sensor Data Management System for providing the MSTAR IU Mixed Targets dataset. We understand the effort and resources invested in curating and maintaining this valuable dataset, which forms the foundation of our project. To acknowledge and give credit to the Sensor Data Management System, we will prominently mention their contribution in all project publications, reports, and presentations. We will provide appropriate citations and include a statement recognizing their dataset as the source of our training and evaluation data.

Methodology:

1. Data Preprocessing: We will preprocess the MSTAR IU Mixed Targets dataset to enhance its compatibility with YOLOv8 object detection algorithm. Involve resizing, normalizing, and augmenting the images.

2. Training and Evaluation: The selected model will be trained on the preprocessed dataset, utilizing appropriate loss functions and optimization techniques. We will extensively evaluate the model's performance using standard evaluation metrics such as precision, recall, and mean average precision (mAP).

3. Fine-tuning and Optimization: We will fine-tune the model on the MSTAR IU Mixed Targets dataset to enhance its accuracy and adaptability to SAR-specific features. Additionally, we will explore techniques such as transfer learning and data augmentation to further improve the model's performance.

4. Results and Analysis: The final model's performance will be analyzed in terms of detection accuracy, computational efficiency, and generalization capability. We will conduct comprehensive experiments and provide visualizations to showcase the model's object detection capabilities on the MSTAR IU Mixed Targets dataset.

5. Model Selection and Revaluation: We will evaluate and compare state-of-the-art object detection models to identify the most suitable architecture for SAR imagery. This will involve researching and implementing models such as Faster R-CNN, other YOLO versions or SSD, considering their performance, speed, and adaptability to the MSTAR dataset.

Conclusion: This project aims to contribute to the field of object detection in SAR imagery by leveraging the valuable MSTAR IU Mixed Targets dataset provided by the Sensor Data Management System. We will ensure ethical use of the data and give proper credit to the dataset's source. By developing an accurate and efficient object detection model, we hope to advance the understanding and application of SAR imagery in various domains.

Note: This project description serves as an overview and can be expanded upon in terms of specific methodologies, experiments, and evaluation techniques as the project progresses.

Palm Fruit Ripeness Detection

## Overview

Palm Fruit Ripeness Detection is a dataset for object detection tasks - it contains Palm Fruit annotations for 4,160 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).

Sam2_Fine Tune

## Overview

Sam2_Fine Tune is a dataset for instance segmentation tasks - it contains Wafer IB4N annotations for 389 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).

Dataset_fine_tune V2l

## Overview

Dataset_fine_tune V2l is a dataset for object detection tasks - it contains Cars annotations for 4,399 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).

BoxingHub Computer Vision Project on Roboflow

https://universe.roboflow.com/boxinghub/boxinghub/

Project Overview

The BoxingHub Computer Vision Project aims to enhance boxing training and education by leveraging advanced AI techniques. This project focuses on developing a computer vision model capable of accurately classifying various boxing punches, such as jabs, crosses, hooks, and uppercuts, using annotated images. By providing real-time feedback to users, the project seeks to improve training effectiveness and self-assessment for boxing enthusiasts of all levels. The model is integrated into a comprehensive web platform, BoxingHub, designed to be an all-in-one resource for boxing knowledge and training.

Descriptions of Each Class Type

6 classes in total - Jab: A quick, straight punch thrown with the lead hand. It is often used to measure distance, set up combinations, and keep the opponent at bay. - Cross: A powerful, straight punch delivered with the rear hand. It typically follows a jab and is used to capitalize on openings created by the lead hand. - Hook: A semi-circular punch thrown with the lead hand, targeting the side of the opponent's head or body. It is effective at close range and often used in combinations. - Uppercut: A vertical punch directed upwards with either hand, aiming for the opponent's chin or body. It is particularly effective against opponents who lean forward or have a low guard. - No Punch: - Bag:

Current Status and Timeline

• Current Status: The project is in the active development phase, focusing on fine-tuning the computer vision model and integrating it into the BoxingHub platform. Initial model training has been completed, and the model is undergoing iterative improvements based on user feedback and additional data collection.
• Timeline:
  • Month 1-2: Data collection and annotation, initial model training.
  • Month 3: Model fine-tuning and validation.
  • Month 4: Integration with the BoxingHub platform and user testing.
  • Month 5: Refinement based on feedback and performance evaluation.
  • Month 6: Final deployment and public release.

Links to External Resources

• BoxingHub Website: Access the platform to explore boxing training resources and test the computer vision model.
• Roboflow Documentation: Comprehensive guide on how to use Roboflow for computer vision projects.
• Annotated Dataset: A link to the publicly available boxing punch dataset used for model training and evaluation.

Contribution and Labeling Guidelines

We welcome contributions from the community to enhance the dataset and model accuracy. Here are some guidelines to ensure consistent data quality:

• Data Quality: Submit high-resolution images that clearly depict the desired punch type. Avoid blurry or low-contrast images that may confuse the model.
• Annotation Accuracy: When labeling images, ensure that the bounding boxes accurately encompass the punching action without including excessive background or irrelevant objects.
• Class Distribution: To maintain balanced model performance, contribute a diverse set of images for each class type (jab, cross, hook, uppercut).
• Ethical Considerations: Ensure all contributed images comply with privacy and copyright regulations. Only submit images you have the right to share and use for training purposes.

By following these guidelines, contributors can help improve the BoxingHub Computer Vision Project, making boxing training more accessible, effective, and data-driven.

SWIR RGB

## Overview

SWIR RGB is a dataset for object detection tasks - it contains CAR TRUCK PEOPLE BIKE annotations for 665 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).

We upload the EMDS-7 dataset of microscopy images of environmental microorganisms which is publicly available here : https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571 and here is the research article that was published which explains what computer vision algorithms achieved when applied to the dataset https://arxiv.org/abs/2110.07723, "EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation", Hechen Y et al. . We are not claiming any credit from the dataset, we only retrieved it from the research team's public media. We were not able to find a dictionary mapping the labels names to the raw classes (there are 42 classes including class "unknown"). It didn't matter to us as our aim is to transfer learn with EMDS-7 after which stage we would apply a second stage fine-tuning on an extremely small manually (by ourselves) annotated dataset. This dataset my bias your computer vision model to detect objects if the background is greenish, also some of the images have the scale of the size which might biais models to detect objects more if there's a scale on test/ future images. If you have any remarks or copyrights issues, we are reachable here : thomas.sadigh@gmail.com

CMP_Image_Segmentation_Fine_Tune_Segformer

## Overview

CMP_Image_Segmentation_Fine_Tune_Segformer is a dataset for semantic segmentation tasks - it contains Facade Buildings annotations for 756 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Use this model to detect when faces appear and are exposed to the camera.

The dataset contains approx. 1,800 images sourced from a variety of environments such as: indoor, outdoor, large crowd, portrait, social media, security camera, etc.

Instances where a person is in the frame but the face is not visible (the person is “facing away”) are not annotated for training. We also included examples where faces are partially covered by sweaters, sunglasses, masks, microphones, etc.

This model was not trained intentionally for very large crowd use (think blurry faces in the distant background). We recommend fine-tuning this model on your own dataset for those use cases.

Example use case: Are people looking in X direction? How long are people looking at X object?