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Related Objects of Interest: mouth , nose , animal , absolutely , against , america , assistance , attack , because , black
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Top Between Computer Vision Models
The models below have been fine-tuned for various between 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 between detection model.
At the bottom of this page, we have guides on how to count betweens in images and videos.
706 images 1342 classes 1 model
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
84 images 52 classes 1 model
+ . 0 1 2 3 4 5 6 7 8 9 A defect will be classified as Bump Defect if : A defect will be classified as Bump surface damage if : A defect will be classified as Die top sise chip/peeling if : A defect will be classified as Particle if : Adaptive Histogram Mode AdaptiveHistogram Area(um2) is greater than or equal to Big Area Status - Bright {area , μ}
704 images 5 classes 1 model
Risk point:Several vehicles are parked in non-parking areas. Risk point:The presence of motorcycles and bicycles within the same lanes as cars. Risk-point:There-are-pedestrians-walking-and-crossing-the-road-in-between-cars. Risk-point:Some-vehicles-appear-to-be-driving-in-the-wrong-direction. Risk-point:The-high-density-of-vehicles-can-lead-to-congestion.
450 images 9 classes 1 model
223 images 1 class 3 models
134 images 30 classes 1 model
ATM ATM opened Attacker Hooded user (male) Inserting card Might have an object (hidden hand) Person Person assisting (spurious activity) Person bending Phone Physical Assault Physical Assault (male) Reaching behind ATM Safe Attempted to be opened Security (male) Setting up Phone (record pin) Stealing customer details (peeking) Threat (fight) USB Un-identifiable
450 images 9 classes 1 model
8.2k images 3 classes 1 model
391 images 13 classes 1 model
(A-4) Simple gap between concrete units with no movement of the wall (E-1)One concrete unit extracted- (E-2)More than one concrete unit extracted- (F-1)One concrete unit out of layer- (F-2)More than one concrete unit out of layer- (G-1)Concrete units need to be rearranged- ArrangementG-1 Gap A-3 Gap A-4 More than one cracked concrete unit One concrete unit out of layer- cracked D-3 disintegrated D-6
40 images 14 classes 1 model
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