Related Objects of Interest: ==============================, * auto-orientation of pixel data (with exif-orientation stripping), the following pre-processing was applied to each image:, the following augmentation was applied to create 3 versions of each source image:, * annotate, and create datasets, * collect & organize images, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, * collaborate with your team on computer vision projects, roboflow is an end-to-end computer vision platform that helps you
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 college
3480 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 2.5 pixels * Random brigthness adjustment of between -25 and +25 percent * Resize to 640x640 (Stretch) * Salt and pepper noise was applied to 14 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 22 23 24 25 26 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, Road Sign Detector - v7 Add blur and noise Roboflow is an end-to-end computer vision platform that helps you
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 KUST
1203 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Grayscale (CRT phosphor) * Resize to 640x640 (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 . 0 2 5 6 7 8 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, LCD - v14 2023-10-13 2:03pm LCD-Display are annotated in YOLOv8 format.
by bcvt
9760 images 33 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 1.5 pixels * Random rotation of between -15 and +15 degrees * 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 21 22 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
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
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
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
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 TEST 1
2254 images 35 classes
chicken train #-Healthy-and-Sick-Chicken-Detection->-2023-02-04-2:29pm *-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 *-Random-exposure-adjustment-of-between--25-and-+25-percent *-Resize-to-416x416-(Stretch) *-annotate *-collaborate-with-your-team-on-computer-vision-projects *-collect-&-organize-images *-export *-understand-and-search-unstructured-image-data *-use-active-learning-to-improve-your-dataset-over-time 1WOC-are-annotated-in-Tensorflow-Object-Detection-format. 2024-at-10:31-AM-GMT ============================== For-state-of-the-art-Computer-Vision-training-notebooks-you-can-use-with-this-dataset Healthy-and-Sick-Chicken-Detection---v18-2023-02-04-2:29pm
by York
246 images 27 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 * Random Gaussian blur of between 0 and 0.5 pixels * Random brigthness adjustment of between -16 and +16 percent * Random exposure adjustment of between -6 and +6 percent * Resize to 600x600 (Fit (white edges)) 15 16 17 18 19 20 21 ============================== Chocolates are annotated in YOLO v5 PyTorch format. Dark Marzipan It includes 267 images. Milk California Brittle
by KKJJ8899
760 images 79 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -3 and +3 percent * Resize to 640x640 (Fit within) * 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 20 21 22 23 24 25 26 27 28 29 30
by Amrutha
1720 images 26 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 1.25 pixels * Random brigthness adjustment of between -25 and +25 percent * Random rotation of between -5 and +5 degrees * Random shear of between -5° to +5° horizontally and -5° to +5° vertically * 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 unstructured image data * use active learning to improve your dataset over time 22 23 24 25 ============================== American Sign Language Letters - v1 v1
40 images 4339 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 6V" "component text" "100 CFK 7BD" "component text" "100 CFK- 7BD" "component text" "100 VFK- 87"