Overview

The Weeds dataset is a collection of garden weeds that can easily confuse object detection models due to similiarity of the weeds compared to its surroundings. This dataset was used with YOLOR for object detection to detect weeds in complex backgrounds.

Example Footage!

Training and Deployment

The weeds model has been trained in Roboflow, available for inference on the Dataset tab.

One could also build a Weeds Detector using YOLOR. This is achieved using the Roboflow Platform which you can deploy the model for robust and real-time detections. You can learn more here: https://augmentedstartups.info/YOLOR-Get-Started

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. With over 92k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality

This project was created by Arfiani Nur Sayidah and is for sorting "apples" from "damaged apples."

The classes are "apple" and "damaged_apples"
Original Class Balance:

1. apple: 2,152
2. damaged_apple: 708

Soybeans kernels counter

Background Information

This dataset was curated and annotated by Mohamed Traore from the Roboflow Team. A custom dataset composed of one class (chicken). The main objective is to identify chicken(s) and perform object-tracking on chicken(s) using Roboflow's "zero shot object tracking."

The original video is from Wendy Thomas (Description: "Definitive proof that the chicken crossed the road to get to the other side.")

The original custom dataset (v1) is composed of 106 images of chickens and their surrounding environment.

The dataset is available under the Public License.

Zero Shot Object Tracking

• Using the video from Wendy Thomas (which was included in this dataset through the use of Roboflow's Video Ingestion tool

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.

Dataset Versions

Version 1 (v1) - 106 images

• Preprocessing: Auto-Orient
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 106 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 3 (v3), "v1-augmented-COCO-transferLearning" - 254 images

Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow

• 3x image generation

Version 11 (v11), "v1-augmented-trainFromScratch" - 463 images

Trained from the Version 3 training checkpoint.

• Modify Classes was applied to remap the "chickens" class to "rooster" (meaning "rooster" will show up for the bounding boxes when running inference).
• 3x image generation

Version 12 (v12) - 185 images

• Preprocessing: Auto-Orient, Modify Classes (remap the "chickens" class to "rooster")
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Mohamed Traore - LinkedIn

The Apple Vision annotated data set contains over 350 images of naturally growing apples on an apple tree. Unlike other existing sets, this set attempted to capture apples growing on trees with different exposures of natural light during the daytime.

The training data was comprised of 77 photos taken of Peter Bloch’s home apple tree. These images were shot between July and September of 2021 on an iPhone 11 camera. After the photos were taken, they were sliced into multiple smaller images with a resolution of 360 × 640 pixels per image. This number was selected as the lowest natural resolution for a CV camera later used in this project.

This set was originally created for the ECE 31 Capstone project at Oregon State University.

Background Information

This dataset was curated and annotated by Ahmed Elmogtaba Abdelaziz.

The original dataset (v6) is composed of 204 images of honeybees present in a wide variety of scenes.

The dataset is available under a Public License.

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.

Dataset Versions

Version 5 - 490 images

• Preprocessing: Resize, 416 by 416
• Augmentations:
• 90° Rotate: Clockwise, Counter-Clockwise
  Rotation: Between -15° and +15°
  Saturation: Between -10% and +10%
  Brightness: Between -10% and +10%
  Blur: Up to 0.25px
  Mosaic: Applied
• Output: 3x image generation

This Dataset contains images of popular North American mushrooms, Chicken of the Woods and Chanterelle, differentiating between the two species.

This dataset is an example of an object detection task that is possible via custom training with Roboflow.

Two versions are listed. "416x416" is a 416 resolution version that contains the base images in the dataset. "416x416augmented" contains the same images with various image augmentations applied to build a more robust model.

This Dataset contains images of popular North American mushrooms, Chicken of the Woods and Chanterelle, differentiating between the two species.

This dataset is an example of an object detection task that is possible via custom training with Roboflow.

Two versions are listed. "416x416" is a 416 resolution version that contains the base images in the dataset. "416x416augmented" contains the same images with various image augmentations applied to build a more robust model.

Background Information

This dataset was curated and annotated by - Karel Cornelis.

The original dataset (v1) is composed of 516 images of various ingredients inside a fridge. The project was created as part of a groupwork for a postgraduate applied AI at Erasmus Brussels - we made an object detection model to identify ingredients in a fridge.

From the recipe dataset we used (which is a subset of the recipe1M dataset) we distilled the top50 ingredients and used 30 of those to randomly fill our fridge.

Read this blog post to learn more about the model production process: How I Used Computer Vision to Make Sense of My Fridge

Watch this video to see the model in action: AICook

The dataset is available under the MIT License.

Getting Started

You can download this dataset for use within your own project, fork it into a workspace on Roboflow to create your own model, or test one of the trained versions within the app.

Dataset Versions

Version 1 (v1) - 516 images (original-images)

• Preprocessing: Auto-Orient
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 3,050 images (aicook-augmented-trainFromCOCO)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8
    Rotation: Between -3° and +3°
    Exposure: Between -20% and +20%
    Blur: Up to 3px
    Noise: Up to 5% of pixels
    Cutout: 12 boxes with 10% size each
• Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
  • mAP = 97.6%, precision = 86.9%, recall = 98.5%

Version 3 (v3) - 3,050 images (aicook-augmented-trainFromScratch)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8
    Rotation: Between -3° and +3°
    Exposure: Between -20% and +20%
    Blur: Up to 3px
    Noise: Up to 5% of pixels
    Cutout: 12 boxes with 10% size each
• Training Metrics: Trained from "scratch" (no transfer learning employed) on Roboflow
  • mAP = 97.9%, precision = 79.6%, recall = 98.6%

Version 4 (v4) - 3,050 images images (aicook-augmented)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8
    Rotation: Between -3° and +3°
    Exposure: Between -20% and +20%
    Blur: Up to 3px
    Noise: Up to 5% of pixels
    Cutout: 12 boxes with 10% size each
• Training Metrics: This version of the dataset was not trained

Karel Cornelis - LinkedIn

Overview

This dataset contains 581 images of various shellfish classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for shellfish!

Use Cases

• Train object detector to differentiate between a lobster, shrimp, and crab.
• Train object dector to differentiate between shellfish
• Object detection dataset across different sub-species
• Object detection among related species
• Test object detector on highly related objects
• Train shellfish detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

This dataset is derived by the following publication:

Kaspars Sudars, Janis Jasko, Ivars Namatevs, Liva Ozola, Niks Badaukis,
Dataset of annotated food crops and weed images for robotic computer vision control,
Data in Brief,
Volume 31,
2020,
105833,
ISSN 2352-3409,
https://doi.org/10.1016/j.dib.2020.105833.
(https://www.sciencedirect.com/science/article/pii/S2352340920307277)
Abstract: Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages
Keywords: Computer vision; Object detection; Image annotation; Precision agriculture; Crop growth and development

Overview

The Aerial Sheep dataset contains images taken from a birds-eye view with instances of sheep in them. Images do not differentiate between gender or breed of sheep, instead grouping them into a single class named "sheep".

Example Footage

See RIIS's sheep counter application for additional use case examples.
Link - https://riis.com/blog/counting-sheep-using-drones-and-ai/

About RIIS

https://riis.com/about/

Banana Ripening Process Dataset and Model

This dataset contains images of the classes below:

• freshripe
• freshunripe
• overripe
• ripe
• rotten
• unripe

Usage

This is an object detection model that can be used to possibly identify where in the Fruit Ripening Process fruit at stores are and when to take them off the shelves and put them in composting.

Overview

The PlantDoc dataset was originally published by researchers at the Indian Institute of Technology, and described in depth in their paper. One of the paper’s authors, Pratik Kayal, shared the object detection dataset available on GitHub.

PlantDoc is a dataset of 2,569 images across 13 plant species and 30 classes (diseased and healthy) for image classification and object detection. There are 8,851 labels. Read more about how the version available on Roboflow improves on the original version here.

And here's an example image:

Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 416x416 export.

Use Cases

As the researchers from IIT stated in their paper, “plant diseases alone cost the global economy around US$220 billion annually.” Training models to recognize plant diseases earlier dramatically increases yield potential.

The dataset also serves as a useful open dataset for benchmarks. The researchers trained both object detection models like MobileNet and Faster-RCNN and image classification models like VGG16, InceptionV3, and InceptionResnet V2.

The dataset is useful for advancing general agriculture computer vision tasks, whether that be health crop classification, plant disease classification, or plant disease objection.

Using this Dataset

This dataset follows Creative Commons 4.0 protocol. You may use it commercially without Liability, Trademark use, Patent use, or Warranty.

Provide the following citation for the original authors:

@misc{singh2019plantdoc,
                                title={PlantDoc: A Dataset for Visual Plant Disease Detection},
                                author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra},
                                year={2019},
                                eprint={1911.10317},
                                archivePrefix={arXiv},
                                primaryClass={cs.CV}
                            }
                            

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Overview

The Fruits dataset is an image classification dataset of various fruits against white backgrounds from various angles, originally open sourced by GitHub user horea. This is a subset of that full dataset.

Example Image:

Use Cases

Build a fruit classifier! This could be a just-for-fun project just as much as you could be building a color sorter for agricultural use cases before fruits make their way to market.

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.