Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, * annotate, and create datasets, * auto-orientation of pixel data (with exif-orientation stripping), * collect & organize images, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, the following augmentation was applied to create 3 versions of each source image:, * collaborate with your team on computer vision projects, * understand and search unstructured image data
Top Source Computer Vision Models
The models below have been fine-tuned for various source detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own source detection model.
At the bottom of this page, we have guides on how to count sources in images and videos.
by James Mixon
5399 images 73 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random rotation of between -5 and +5 degrees * Randomly crop between 0 and 30 percent of the image * Resize to 640x352 (Fit within) * Salt and pepper noise was applied to 0 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 0 1 10 100 1000 10th Spot 11 12 13
by UM
9757 images 100 classes
by Steven Kuo
1019 images 852 classes
Arch-Category_Analytics Arch-Category_Application-Integration Arch-Category_Blockchain Arch-Category_Business-Applications Arch-Category_Cloud-Financial-Management Arch-Category_Compute Arch-Category_Containers Arch-Category_Customer-Enablement Arch-Category_Database Arch-Category_Developer-Tools Arch-Category_End-User-Computing Arch-Category_Front-End-Web-Mobile Arch-Category_Games Arch-Category_Internet-of-Things Arch-Category_Management-Governance Arch-Category_Media-Services Arch-Category_Migration-Transfer Arch-Category_Networking-Content-Delivery Arch-Category_Quantum-Technologies Arch-Category_Robotics
55 images 8 classes
by eVeDrug
2000 images 27 classes
Age Concomitant Drugs and Dates of Administration Country DOB Daily Dose Date Received by Manufacturer Date of this Report Did Reaction Abate Did Reaction Reappear Indication For Use Initials MFR Control Number Name and Address of Reporter Name and Addresss of Manufacturer Other Relevant History Reaction Onset Reactions Remarks Report Source Report Type
by go4av05
8659 images 40 classes
* Random Gaussian blur of between 0 and 2.5 pixels * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -25 and +25 percent * Salt and pepper noise was applied to 5 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 17 18 19 20 21 22 23 24 25 26
by Paridhi
43 images 34 classes
150 images 4 classes
by max jay
1672 images 804 classes
Arch-Category_Customer-Enablement Arch-Category_Developer-Tools Arch-Category_Games Arch-Category_Networking-Content-Delivery Arch-Category_Quantum-Technologies Arch-Category_Robotics Arch-Category_Serverless Arch-Category_VR-AR Arch_AWS-Amplify Arch_AWS-App-Mesh Arch_AWS-App-Runner Arch_AWS-AppConfig Arch_AWS-AppSync Arch_AWS-Application-Auto-Scaling Arch_AWS-Application-Composer Arch_AWS-Application-Cost-Profiler Arch_AWS-Application-Discovery-Service Arch_AWS-Application-Migration-Service Arch_AWS-Auto-Scaling Arch_AWS-Backint-Agent
42 images 3373 classes
"capacitor jumper" CJ1 "capacitor jumper" CJ2 "component text" "-309 LL6" "component text" "0 N" "component text" "0001 5293 170A" "component text" "021 LDBM N389" "component text" "0821-1X1T-43-F 1402 WM" "component text" "0833 LTC2274 UJ BT267910" "component text" "085811 B4T EHCR" "component text" "0N 5C" "component text" "1 2 3 4" "component text" "1 2" "component text" "100 CFK 7BD" "component text" "100 CFK- 7BD" "component text" "100 VFK- 87" "component text" "100 VFK- 8R7" "component text" "106C 43JJ2" "component text" "107A 938H4" "component text" "12-000" "component text" "15 203"
by test
200 images 22 classes
* 50% probability of horizontal flip * 50% probability of vertical flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down * Resize to 600x600 (Fit (white edges)) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time ============================== Chocolates are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, Roboflow is an end-to-end computer vision platform that helps you The dataset includes 267 images. The following augmentation was applied to create 5 versions of each source image: The following pre-processing was applied to each image: This dataset was exported via roboflow.com on June 4, 2023 at 12:27 PM GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
9472 images 100 classes
by dev1
80 images 29 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down * Random rotation of between -15 and +15 degrees * Resize to 416x416 (Stretch) * Salt and pepper noise was applied to 5 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand unstructured image data * use active learning to improve your dataset over time 20 21 22 23 24 25 26 27
by Test
4176 images 43 classes
object * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random rotation of between -45 and +45 degrees * Resize to 640x640 (Stretch) * annotate, and create datasets * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 20 21 22 23 24 25 26 27 28 29 30
3006 images 38 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise * Random rotation of between -15 and +15 degrees * Randomly crop between 0 and 20 percent of the image * Resize to 416x416 (Stretch) 13 14 15 16 17 18 19 20 21 22 23 24 25 26
by PCB
40 images 4491 classes
"capacitor jumper" CJ1 "capacitor jumper" CJ2 "component text" " CC BE 10000000" "component text" "-309 LL6" "component text" "0 1 2 3 4 5 6 7" "component text" "0 N" "component text" "0001 5293 170A" "component text" "0123456789ABCDEF" "component text" "021 LDBM N389" "component text" "0821-1X1T-43-F 1402 WM" "component text" "0833 LTC2274 UJ BT267910" "component text" "085811 B4T EHCR" "component text" "1 2 3 4 5 6 7 8" "component text" "1 2 3 4" "component text" "1 2 3" "component text" "1 2" "component text" "100 25V UT" "component text" "100 50V UT" "component text" "100 6V" "component text" "100 CFK 7BD"
by ingredients
9335 images 38 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise * Random rotation of between -15 and +15 degrees * Randomly crop between 0 and 20 percent of the image * Resize to 416x416 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 23 24 25 26 27 28 29 30
40 images 4491 classes
"capacitor jumper" CJ1 "capacitor jumper" CJ2 "component text" " CC BE 10000000" "component text" "-309 LL6" "component text" "0 1 2 3 4 5 6 7" "component text" "0 N" "component text" "0001 5293 170A" "component text" "0123456789ABCDEF" "component text" "021 LDBM N389" "component text" "0821-1X1T-43-F 1402 WM" "component text" "0833 LTC2274 UJ BT267910" "component text" "085811 B4T EHCR" "component text" "1 2 3 4 5 6 7 8" "component text" "1 2 3 4" "component text" "1 2 3" "component text" "1 2" "component text" "100 25V UT" "component text" "100 50V UT" "component text" "100 6V" "component text" "100 CFK 7BD"
by Crickets
6558 images 52 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random rotation of between -15 and +15 degrees * Resize to 640x640 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 23 24 25 26 27 28 30 31 32 33
1994 images 37 classes
* 50% probability of horizontal flip * 50% probability of vertical flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise * Resize to 180x300 (Stretch) * Salt and pepper noise was applied to 1 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 24 25 26 27 28 29 30 31