Related Objects of Interest: * auto-orientation of pixel data (with exif-orientation stripping), ==============================, the following pre-processing was applied to each image:, * 50% probability of horizontal flip, * equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down, the following augmentation was applied to create 3 versions of each source image:, * annotate, and create datasets, * collaborate with your team on computer vision projects, * collect & organize images, * export, train, and deploy computer vision models
1 - 30 of 100k+
by sanu
87 images 496 classes
pipe yellow (1") CPVC PRO 45 DEG ELBOW (1") CPVC PRO COUPLER SDR 11 (1") CPVC PRO F.A.P.T SDR 11 (1") CPVC PRO PIPE CTS SDR11 5M (1") CPVC PRO TEE SDR 11 (1-1/4") CPVC PRO 45 DEG ELBOW (1-1/4") CPVC PRO 90 DEG ELBOW (1-1/4") CPVC PRO COUPLER SDR 11 (1-1/4") CPVC PRO F.A.P.T SDR 11 (1-1/4") CPVC PRO PIPE CTS SDR 11 5M (1-1/4") CPVC PRO TEE SDR 11 (1-1/4"x1") CPVC PRO COUPLER SDR 11 (1-1/4"x1") CPVC PRO TEE SDR 11 (3/4") CPVC PRO M.A.B.T SDR 11 (3/4") CPVC PRO PIPE CTS SDR 11 5M (3/4"x1/2") CPVC PRO BRS FPT 90 ELB (3/4"x1/2") CPVC PRO BRS FPT TEE SDR 11 (Brass)
by akash
781 images 209 classes
ADOPT A HIGHWAY (ADOT)_D14-101 ADVANCE GUIDE 1 LINE 2 LINE DESTINATION DISTANCE_E1-102A ADVANCE STREET NAME (1-2-3 LINE)_D3-(2_2R_2S) ADVANCE TURN ARROW AUXILIARY - 90 DEGREE - INTERSTATE_M5-1 ADVANCE TURN ARROW AUXILIARY - 90 DEGREE_M5-1 AHEAD (PLAQUE)_R3-17AP AHEAD (PLAQUE)_W16-9P AIRPORT_I-5 ARM_BRIDGE ARM_CANTILEVER ARM_DOUBLEMAST ARM_SINGLEMAST ARM_SPANWIRE ARM_STEELDOUBLE ARM_STEELSINGLE BE PREPARED TO STOP_W3-4 BEGIN HIGHER FINES ZONE_R2-10 BEGIN_M4-14 BICYCLE (SYMBOL)_W11-1 BICYCLE OR PEDESTRIAN (SYMBOL)_W11-15
by Garbage
781 images 209 classes
ADOPT A HIGHWAY (ADOT)_D14-101 ADVANCE GUIDE 1 LINE 2 LINE DESTINATION DISTANCE_E1-102A ADVANCE STREET NAME (1-2-3 LINE)_D3-(2_2R_2S) ADVANCE TURN ARROW AUXILIARY - 90 DEGREE - INTERSTATE_M5-1 ADVANCE TURN ARROW AUXILIARY - 90 DEGREE_M5-1 AHEAD (PLAQUE)_R3-17AP AHEAD (PLAQUE)_W16-9P AIRPORT_I-5 ARM_BRIDGE ARM_CANTILEVER ARM_DOUBLEMAST ARM_SINGLEMAST ARM_SPANWIRE ARM_STEELDOUBLE ARM_STEELSINGLE BE PREPARED TO STOP_W3-4 BEGIN HIGHER FINES ZONE_R2-10 BEGIN_M4-14 BICYCLE (SYMBOL)_W11-1 BICYCLE OR PEDESTRIAN (SYMBOL)_W11-15
by Road Traffic
9866 images 398 classes
complementary--accident-area--g3 complementary--both-directions--g1 complementary--buses--g1 complementary--chevron-left--g1 complementary--chevron-left--g2 complementary--chevron-left--g3 complementary--chevron-left--g4 complementary--chevron-left--g5 complementary--chevron-right--g1 complementary--chevron-right--g3 complementary--chevron-right--g4 complementary--chevron-right--g5 complementary--chevron-right-unsure--g6 complementary--distance--g1 complementary--distance--g2 complementary--distance--g3 complementary--except-bicycles--g1 complementary--extent-of-prohibition-area-both-direction--g1 complementary--go-left--g1 complementary--go-right--g1
by Marco
9560 images 52 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 1.75 pixels * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -15 and +15 percent * Random rotation of between -10 and +10 degrees * Random shear of between -2° to +2° horizontally and -2° to +2° vertically * Randomly crop between 0 and 15 percent of the image * Resize to 640x640 (Stretch) * Salt and pepper noise was applied to 2 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 29 30
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
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) * 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
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 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 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 Mostra
41773 images 401 classes
complementary--accident-area--g3 complementary--both-directions--g1 complementary--buses--g1 complementary--chevron-left--g1 complementary--chevron-left--g2 complementary--chevron-left--g3 complementary--chevron-left--g4 complementary--chevron-left--g5 complementary--chevron-right--g1 complementary--chevron-right--g3 complementary--chevron-right--g4 complementary--chevron-right--g5 complementary--chevron-right-unsure--g6 complementary--distance--g1 complementary--distance--g2 complementary--distance--g3 complementary--except-bicycles--g1 complementary--extent-of-prohibition-area-both-direction--g1 complementary--go-left--g1 complementary--go-right--g1
by Deneme
3362 images 694 classes
* Auto-contrast via contrast stretching * 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 shear of between -14° to +14° horizontally and -15° to +15° vertically * Resize to 800x800 (Stretch) * Salt and pepper noise was applied to 1.13 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 100 101 102 103 104 105 106 107
by Deneme
3256 images 692 classes
* Auto-contrast via contrast stretching * 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 shear of between -14° to +14° horizontally and -15° to +15° vertically * Resize to 800x800 (Stretch) * Salt and pepper noise was applied to 1.13 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 100 101 102 103 104 105 106 107
by nina
245 images 31 classes
chocolate * 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. It includes 267 images. The following augmentation was applied to create 5 versions of each source image:
by chocolate
244 images 31 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)) -black chocolate -brown chocolate -gift chocolate -wave chocolate -white chocolate 15 16 17 18 19 20 21
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