Overview:

Football (soccer) player and football (soccer) ball detection dataset from Augmented Startups. * Project Type: Object Detection * Labeled/Annotated with: Bounding boxes

Classes:

• football, player

How to Use:

This is a great starter-dataset for those wanting to test player and/or ball-tracking for football (soccer) games with the Deploy Tab, or the Deployment device and method of their choice.

Images can also be Cloned to another project to continue iterating on the project and model. World Cup, Premier League, La Liga, Major League Soccer (MLS) and/or Champions League computer vision projects, anyone?

Roboflow offers AutoML model training - Roboflow Train, and the ability to import and export up to 30 different annotation formats. Leaving you flexibility to deploy directly with a Roboflow Train model, or use Roboflow to prepare and manage datasets, and train and deploy with the custom model architecture of your choice + https://github.com/roboflow-ai/notebooks.

Tips for Model and Dataset Improvement:

• https://blog.roboflow.com/pip-install-roboflow
• https://help.roboflow.com/implementing-active-learning

This dataset, "Hardware Object Detection," is specifically curated for identifying various hardware components. It's designed to support computer vision tasks related to the detection of items such as black components, defects, long screws, nails, nuts, rivets, tek screws, and washers. This dataset is integral to training robust object detection models for industrial or quality control applications.

notebooks: https://github.com/erwincarlogonzales?tab=repositories android: https://github.com/erwincarlogonzales/mldetection-android-firebase Jetson Nano: https://github.com/erwincarlogonzales/mldetection-jetson-nano

Overview

The Insect_Detect_classification_v2 dataset contains mainly images of various insects sitting on or flying above an artificial flower platform. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring.

Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the first dataset version were transferred to this dataset.

The images in this dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use the uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided training notebook.

Classes

This dataset contains the following 27 classes:

• ant (Formicidae)
• bee (Anthophila excluding Apis mellifera and Bombus sp.)
• bee_apis (Apis mellifera)
• bee_bombus (Bombus sp.)
• beetle (Coleoptera excluding Coccinellidae and some Oedemeridae)
• beetle_cocci (Coccinellidae)
• beetle_oedem (visually distinct Oedemeridae)
• bug (Heteroptera excluding Graphosoma italicum)
• bug_grapho (Graphosoma italicum)
• fly (Brachycera excluding Empididae, Sarcophagidae, Syrphidae and small Brachycera)
• fly_empi (Empididae)
• fly_sarco (visually distinct Sarcophagidae)
• fly_small (small Brachycera)
• hfly_episyr (hoverfly Episyrphus balteatus)
• hfly_eristal (hoverfly Eristalis sp., mainly Eristalis tenax)
• hfly_eupeo (mainly hoverfly Eupeodes corollae and Scaeva pyrastri)
• hfly_myathr (hoverfly Myathropa florea)
• hfly_sphaero (hoverfly Sphaerophoria sp., mainly Sphaerophoria scripta)
• hfly_syrphus (mainly hoverfly Syrphus sp.)
• lepi (Lepidoptera)
• none_bg (images with no insect - background (platform))
• none_bird (images with no insect - bird sitting on platform)
• none_dirt (images with no insect - leaves and other plant material, bird droppings)
• none_shadow (images with no insect - shadows of insects or surrounding plants)
• other (other Arthropods, including various Hymenoptera and Symphyta, Diptera, Orthoptera, Auchenorrhyncha, Neuroptera, Araneae)
• scorpionfly (Panorpa sp.)
• wasp (mainly Vespula sp. and Polistes dominula)

For the classes hfly_eupeo and hfly_syrphus a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that ar

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Project Overview: The Caridina and Neocaridina Shrimp Detection Project aims to develop and improve computer vision algorithms for detecting and distinguishing between different shrimp varieties. This project is centered around aquarium fish keeping hobbyist and how computer vision can be beneficial to improving the care of dwarf shrimp. This project will focus on zoning a feeding area and tracking and counting caridina shrimp in area.

Caridina and neo-caridina shrimp are two distinct species that require different water parameters for optimal health. Neocaridina shrimp are generally more hardy and easier to keep than caridina species, while caridina shrimp are known for their striking distinctive patterns. The body structure of both species are similar. However, there are specific features that should allow enough sensitivity to at least distinguish between caridina shrimp.

Descriptions of Each Class Type: The dataset for this project includes thirteen different class types. The neo-caridina species have been grouped together to test if the model can distinguish between caridina and neo-caridina shrimp. The remaining classes are all different types of caridina shrimp.

The RGalaxyPinto and BGalaxyPinto varieties are caridina shrimp, with the only difference being their color: one is wine-red while the other dark-blue-black. Both varieties have distinctive spots on the head region and stripes on their backs, making them ideal for testing the model's ability to distinguish between color.

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https://drive.google.com/uc?id=1Asa3DwuWop5UDpBThHgGG6otBSyXgJTV" alt="">

The CRS-CBS Crystal Red Shrimp and Crystal Black Shrimp have similar patterns to the Panda Bee shrimp, but the hues are different. Panda shrimp tend to be a deeper and richer color than CRS-CBS shrimp, CRS-CBS tend to have thicker white rings.

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https://drive.google.com/uc?id=19yO42UW_ai11Da3KgaEiUEHn0OnJc0As" alt="">

The Panda Bee variety, on the other hand, is known for its panda-like pattern white and black/red rings.The color rings tend to be thicker and more pronounced than the Crystal Red/Black Shrimp.

Within the Caridina species, there are various tiger varieties. These include Fancy Tiger, Raccoon Tiger, Tangerine Tiger, Orange Eyed Tiger (Blonde and Full Body). All of these have stripes along the sides of their bodies. Fancy Tiger shrimp have a similar color to CRS, but with a tiger stripe pattern. Raccoon Tiger and Orange Eyed Tiger Blonde look very similar, but the body of the Raccoon Tiger appears larger, and the Orange Eyed Tiger is known for its orange eyes. Tangerine Tigers vary in stripe pattern and can often be confused with certain neo-caridina, specifically yellow or orange varieties.

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https://drive.google.com/uc?id=19ukHly3uZ05FeGdW_hVBWwlHRFvgnMMC" alt="">	https://drive.google.com/uc?id=1AztJj471aIWcRYHNC1lrJse7raO2dUqm" alt="">

The remaining are popular favorites for breeding and distinct color patterns namely Bluebolt, Shadow Mosura, White Bee/Golden Bee, and King Kong Bee.

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https://drive.google.com/uc?id=1AbbCVRnlIQL1MlqY3MJnX9t2WVdyq2zJ" alt="">

Links to External Resources: Here are some resources that provide additional information on the shrimp varieties and other resources used in this project:

Caridina Shrimp: https://en.wikipedia.org/wiki/Bee_shrimp
Neo-Caridina Shrimp: https://en.wikipedia.org/wiki/Neocaridina
  Roboflow Polygon Zoning/Tracking/Counting:https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb
  Roboflow SAM: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb
  Ultralytics Hub:https://github.com/ultralytics/hub

Dataset for electronic device detection

The objects detected using this dataset are the following: - Phone, - Laptop, - USB stick, - Keyboard - Router, - Keys, - Server rack, - Mouse

The dataset was created as part of a Machine Learning MOOC. The complete project with the final model and testing can be found on my GitHub

Please use the last version of the dataset.

Background

The Anki Vector robot (assets currently owned by Digital Dream Labs LLC which bought Anki assets in 2019) was first introduced in 2018. In my opinion, the Vector robot has been the cheapest fully functional autonomous robot that has ever been built. The Vector robot can be trained to recognize people; however Vector does not have the ability to recognize another Vector. This dataset has been designed to allow one to train a model which can detect a Vector robot in the camera feed of another Vector robot.

Details Pictures were taken with Vector’s camera with another Vector facing it and had this other Vector could move freely. This allowed pictures to be captured from different angles. These pictures were then labeled by marking the rectangular regions around Vector in all the images with the help of a free Linux utility called labelImg. Different backgrounds and lighting conditions were used to take the pictures. There is also a collection of pictures without Vector.

Example An example use case is available in my Google Colab notebook, a version of which can be found in my Git.

More More details are available in this article on my blog. If you are new to Computer Vision/ Deep Learning/ AI, you can consider my course on 'Learn AI with a Robot' which attempts to teach AI based on the AI4K12.org curriculum. There are more details available in this post.