Top Over Datasets and Models
The datasets below can be used to train fine-tuned models for over detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
At the bottom of this page, we have guides on how to train a model using the over datasets below.
by nada majd
9940 images 71 classes
bike bus car motor person rider traffic light traffic sign train truck car_ahead cross walk information--pedestrians-crossing--g1 not_relevant green not_relevant red not_relevant yellow regulatory--give-way-to-oncoming-traffic--g1 regulatory--go-straight--g1 regulatory--go-straight-or-turn-left--g1 regulatory--go-straight-or-turn-right--g1
by DICE
9896 images 71 classes
bike bus car motor person rider traffic light traffic sign train truck car_ahead cross walk information--pedestrians-crossing--g1 not_relevant green not_relevant red not_relevant yellow regulatory--give-way-to-oncoming-traffic--g1 regulatory--go-straight--g1 regulatory--go-straight-or-turn-left--g1 regulatory--go-straight-or-turn-right--g1
by Archangel
640 images 21 classes
-bike/motorcycle- -boat- -car- -construction vehicle- -person bending over (overhead)- -person bending over- -person climbing- -person crawling- -person crouching- -person kneeling- -person laying down- -person leaning- -person on hands and knees- -person pointing- -person riding bike/motorcycle- -person sitting- -person squatting- -person standing (overhead)- -person standing- -person-
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 NEDUET
759 images 46 classes
* 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 ============================== Account No Carrier Column Customer Customer No Customer Order Date Customer PO Number Date Shipped Demo - v27 Terex Description Footer Section For state of the art Computer Vision training notebooks you can use with this dataset,
3941 images 29 classes
hand Boil-Over-Warning Boiling-Water-Saucepan Bolognese-Pan Bread-Slice Buttered-Bread Carrot Chopped-Carrots Chopped-Red-Onion Chopped-White-Onion Diagonally-Cut-Sandwich Empty-Pan Horizontal-Cut-Sandwich Jam-Bread Mixed-Herbs Onion-Carrot-Tomato-Pan Pasta-Cooking-Saucepan Pepper Quorn Raw-Pasta-Saucepan
by NO
10053 images 69 classes
bike bus car motor person rider traffic sign train truck cross walk information--pedestrians-crossing--g1 not_relevant green not_relevant red not_relevant yellow regulatory--give-way-to-oncoming-traffic--g1 regulatory--go-straight--g1 regulatory--go-straight-or-turn-left--g1 regulatory--go-straight-or-turn-right--g1 regulatory--keep-left--g1 regulatory--keep-right--g1
by osmando
706 images 1342 classes
animal car drink mushroom rock wine glass 2 bottles on a rock next to fruit in front of orange background. 2 brown paper boxes in front of light brown background 2 cream bottles in front of blue tiles 2 macarons on a rock block in front of light orange background 3 black bottles next to yellow stairs and dry flower with shadow 3 blue bottles on blue plates next to dry nuts 3 bottles in front of light background with shadows 3 bottles in front of light brown background with shadows. 3 bottles in front of light gray background with shadows of a plant. 3 boxes in front of dark background 3 perfume bottles on reflective gray surface 4 paper boxes in front of light yellow background 4 potteries on a marble table 6 white bowls of spices on a wooden surface
by SHL
1164 images 6 classes
2349 images 15 classes
299 images 562 classes
by gtsrbanno
4706 images 50 classes
106 images 3398 classes
by FOUR I
274 images 8 classes
by Zhao Zixi
1884 images 6 classes
by Dung Le
1828 images 43 classes
Ahead only Beware of ice/snow Bicycles crossing Bumpy road Children crossing Dangerous curve left Dangerous curve right Double curve End no passing veh>3.5 tons End of no passing End of speed limit (80km/h) End speed + passing limits General caution Go straight or left Go straight or right Keep left Keep right No entry No passing No passing veh over 3.5 tons
by GRPGLMThesis
950 images 19 classes
1570 images 189 classes
15 degrees ABS malfunction ACC Status Indicator (Active_135) ACC Status Indicator (Active_8) ACC Status Indicator (Standby_135) ACC Status Indicator (Standby_8) ACC driver overtaking ACC driver takes over cue AEB Off indicator AUTO mode_vehicle mode Abnormal status of low-voltage power supply system Abnormal tire pressure Advance charging indicator Automatic parking activation Automatic parking on indicator Battery heating Brake system malfunction or low brake fluid level Brake system warning light CLTC Car model with low light on
by janjang
791 images 16 classes
1158 images 57 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 1 10(10pin) 10(4pin) 11 13 14 1400 15 16 17 18 19
by TSR
2930 images 44 classes
Ahead only Beware of ice-snow Bicycles crossing Bumpy road Children crossing Dangerous curve to the left Dangerous curve to the right Double curve End of all speed and passing limits End of no passing End of no passing by vehicles over 3 End of no passing by vehicles over 3.5 metric tons End of speed limit -80 km-h- General caution Go straight or left Go straight or right Keep left Keep right No entry No passing