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Top Manufacturing Datasets
Manufacturing datasets vary across use cases: quality assurance and product inspection, visual detection and monitoring for safety and compliance, automating product assembly processes, inventory management, anomaly detection, data analytics, and reporting/alerts.
See the top 6 computer vision datasets for manufacturing: https://blog.roboflow.com/top-manufacturing-datasets-computer-vision/
Read about manufacturing use cases for computer vision: https://roboflow.com/industries/manufacturing
Fall Detection
dataset images and annotations were cloned from the following projects:
Use Cases:
This dataset can be used to track boxes on an assembly or manufacturing line, or as a starter-dataset for package detection and "defect" detection use cases for boxes.
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For the defect detection use case, one can Clone the images from this project to a new one, and add more examples (labels) of boxes with defects, such as:
damaged corners
,unsealed boxes
, and more. This defect detection model can be built as a single object-detection model, or broken into a "two pass detection" model (identify the box and the defects with object detection --> send the cropped detections of the defects to a classification model to confirm the classification of the defect, and the severity) -
Converting an Object Detection Dataset to Classification with Isolate Objects
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Roboflow: Single Label Classification | Roboflow: Multi-Label Classification
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Formats | Multi-Label Classification Format | OpenAI CLIP Classification Format
Classes:
box
This project is trying to create an efficient computer or machine vision model to detect different kinds of construction equipment in construction sites and we are starting with three classes which are excavators, trucks, and wheel loaders.
The dataset is provided by Mohamed Sabek, a Spring 2022 Master of Science graduate from Arizona State University in Construction Management and Technology.
The raw images (v1) contains:
- 1,532 annotated examples of "excavators"
- 1,269 annotated examples of "dump truck"
- 1,080 annotated examples of "wheel loader"
Note: versions 2 and 3 (v2 and v3) contain the raw images resized at 416 by 416 (stretch to) and 640 by 640 (stretch to) without any augmentations.
Personal Protective Equipment Dataset and Model
This dataset is a collection of images that contains annotations for the classes below:
- goggles
- helmet
- mask
- no-suit
- no_goggles
- no_helmet
- no_mask
- no_shoes
- shoes
- suit
- no_glove
- glove
Usage
Most of these classes are underrepresented and would need to be balanced for better detection. An improved model can be utilized for use cases that'll detect the classes above in order to minimize exposure to hazards that cause serious workplace injuries.
Overview
The Roboflow Mask Wearing iOS
dataset is an object detection dataset of individuals wearing various types of masks and those without masks. A subset of the images were originally collected by Cheng Hsun Teng from Eden Social Welfare Foundation, Taiwan and relabled by the Roboflow team.
Example images (with masks, and without):
Use Cases
One could use this dataset to build a system for detecting if an individual is wearing a mask in a given photo. PPE detection in high-risk work settings, or general health safety settings are other good use cases.
The dataset has a few batches of images collected only from iPhone's, so as to help improve the performance of model predictions on iPhone's with the Roboflow Mobile iOS SDK.
Using this Dataset
Use the Download this Dataset
button to download and import 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.
You can also import this dataset to your own Roboflow account and export it, or continue working on it on Roboflow to test, improve, and deploy your model.
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.
This dataset contains images of 3D printer failures across a variety of print jobs spanning several types of printer, material, models, and colors. It can be used as the starting point for creating a computer vision model that monitors a print job and aborts it as soon as it becomes evident that there is a problem (and alerts you before wasting a ton of time and money on materials). It can also be used to do automated quality assurance on finished models to make sure they do not exhibit common defects.
A dataset of 276 images is provided, along with a pre-trained model you can try in your browser and deploy to several different edge devices.
Overview
The Hard Hat
dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.
Example Image:
Use Cases
One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.
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.
About this Dataset
This dataset was created by exporting images from images.cv and labeling them as an object detection dataset. The dataset contains 421 raw images (v1 - raw-images) and labeled classes include:
- forklift
- person
Background Information
This dataset was curated and annotated by Mohamed Attia.
The original dataset (v1) is composed of 451 images of various pills that are present on a large variety of surfaces and objects.
The dataset is available under the 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 1 (v1) - 451 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
- Augmentations: No augmentations applied
- Training Metrics: This version of the dataset was not trained
Version 2 (v2) - 1,083 images
- Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
- Augmentations:
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 77% Maximum Zoom
Rotation: Between -45° and +45°
Shear: ±15° Horizontal, ±15° Vertical
Hue: Between -22° and +22°
Saturation: Between -27% and +27%
Brightness: Between -33% and +33%
Exposure: Between -25% and +25%
Blur: Up to 3px
Noise: Up to 5% of pixels
Cutout: 3 boxes with 10% size each
Mosaic: Applied
Bounding Box: Brightness: Between -25% and +25% - Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
- mAP = 91.4%, precision = 61.1%, recall = 93.9%
Version 3 (v3) - 1,083 images
- Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
- Augmentations:
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 77% Maximum Zoom
Rotation: Between -45° and +45°
Shear: ±15° Horizontal, ±15° Vertical
Hue: Between -22° and +22°
Saturation: Between -27% and +27%
Brightness: Between -33% and +33%
Exposure: Between -25% and +25%
Blur: Up to 3px
Noise: Up to 5% of pixels
Cutout: 3 boxes with 10% size each
Mosaic: Applied
Bounding Box: Brightness: Between -25% and +25% - Training Metrics: Trained from "scratch" (no transfer learning employed) on Roboflow
- mAP = 84.3%, precision = 53.2%, recall = 86.7%
Version 4 (v4) - 451 images
- Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
- Augmentations: No augmentations applied
- Training Metrics: This version of the dataset was not trained
Version 5 (v5) - 496 images
- Preprocessing: Auto-Orient, all classes remapped (Modify Classes) to "pill", Isolate Objects
- The Isolate Objects preprocessing step was added to convert this object detection project into a suitable format for export in OpenAI's CLIP annotation format so that it could be used as a classifcation model (classification dataset available here: https://universe.roboflow.com/mohamed-attia-e2mor/pill-classification)
Mohamed Attia - LinkedIn
Background Information
This dataset was curated and annotated by Mohamed Attia.
The original dataset (v1) is composed of 451 images of various pills that are present on a large variety of surfaces and objects.
The dataset is available under the 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 1 (v1) - 496 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
- Augmentations: No augmentations applied
- Training Metrics: This version of the dataset was not trained
Version 2 (v2) - 1,190 images
-
Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
-
Augmentations:
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Shear: ±5° Horizontal, ±5° Vertical
Hue: Between -25° and +25°
Saturation: Between -10% and +10%
Brightness: Between -10% and +10%
Exposure: Between -10% and +10%
Noise: Up to 2% of pixels
Cutout: 5 boxes with 5% size each -
Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
NOTE:
The Isolate Objects preprocessing step was added to convert the original object detection project into a suitable format for export in OpenAI's CLIP annotation format so that it could be used as a classifcation model in this project.
Mohamed Attia - LinkedIn
This project was created for research work by
Dainius Varna and Vytautas Abromavičius of Vilnius Gediminias Technical University in Lithuania.
The dataset consists of images of SMD-type electronic components, which are moving on a conveyor belt. There are four types of components in the collected dataset:
- Capacitors
- Resistors
- Diodes
- Transistors
This is the initial dataset that was augmented to create the final model. Download the raw-images dataset version (v2) of this project to start your own custom project.
- A research paper on the project, titled A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt was published in the MDPI Applied Sciences journal on May 31, 2022.
Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), 03227 Vilnius, Lithuania; vgtu@vgtu.lt
- Correspondence: vytautas.abromavicius@vilniustech.lt
Dataset Collection
The dataset was collected using Nvidia Data Capture Control.
Figure 3. Four types of electronic components used for the dataset. (a) capacitor, (b) resistor, (c) diode, (d) transistor.
Abstract:
The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor.
Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems.
This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components.
The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection.
The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform.
Overview
The Hard Hat
dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.
The original dataset has a 75/25 train-test split.
Example Image:
Use Cases
One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.
Using this Dataset
Use the fork
or Download this Dataset
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.
Dataset Versions:
Image Preprocessing | Image Augmentation | Modify Classes
v1
(resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentationsv2
(raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original imagesv3
(v3): generated with the original 75/25 train-test split | Modify Classes used to dropperson
class | Preprocessing and Augmentation appliedv5
(raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to dropperson
classv8
(raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drophead
andperson
classesv9
(raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drophead
andhelmet
classesv10
(raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original imagesv11
(augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Modelv12
(augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to dropperson
class | 3x image generation | Trained with Roboflow's Fast Modelv13
(augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to dropperson
class | 3x image generation | Trained with Roboflow's Accurate Modelv14
(raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to dropperson
class, and remap/relabelhelmet
class tohead
Choosing Between Computer Vision Model Sizes | Roboflow Train
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.
Recyclable Items Dataset and Model
This is an object detection dataset that contains the classes below:
- Plastic
- Glass
- Metal
Usage
This model can be potentially used to detect the objects above in an effort to sort them in a recycling center or an Automated River Cleaning System that uses computer vision.
Garbage Object-Detection to Identify Disposal Class
This dataset detects various kinds of waste, labeling with a class that indentifies how it should be disposed
This dataset was originally created by Vanitchaporn. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/vanitchaporn/circuit-gexit.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Anonymous. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/new-workspace-rt1da/solarpaneldetectmodel.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Sina. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/sina-uyen0/damage_level_detection.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by T, Pankaj, Aniket Dhanotia, Tejasvi Singh, Garvita Vijay, Goku, Kavya Shukla, Aniket Choudhary, Ananya Kharayat, Monika Patel, Pearl Rathour, Devansh Shrivastava, Aniket Dhanotia, Priyansh Urajput, Sejal, Roshni Ghai, Krishna Gambhir, Ayush Sahu, Ujjwal Sharma, Divyam Jha, Kanchan, Kartik Attri, Lav Naruka, Kas, and Preeti Sharma. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/new-workspace-rzrja/pcb-2.0.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Monika Patel, Kartik Attri, Aniket Dhanotia, Divyam Jha, Pankaj, kanchan, Ujjwal Sharma, Garvita Vijay, Aniket Choudhary, Pearl Rathour, Roshni Ghai, Kavya Shukla, Preeti Sharma, Ananya Kharayat, Krishna Gambhir, Lav Naruka, Kas, Sejal, Tejasvi Singh, Ayush Sahu, Pri, Aniket Dhanotia, and Devansh Shrivastava. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/new-workspace-rzrja/pcb-2.0.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Anonymous. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/computer-vision/worker-safety.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Khaingwintz. To see the current project, which may have been updated since this version, please go here.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark
This dataset was originally created by Evan Kim, MJ Kim. To see the current project, which may have been updated since this version, please go here.
This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark