Visual Classification Overview

Visual classification models classify an entire image into different categories by assigning a confidence score for each class.

Classification models can be multi-headed, where each group of mutually exclusive model classes belong to a single model head. For example, when an image is run through Hive's visual moderation model, one head might classify sexually not-safe-for-work (NSFW) content while another head might classify the presence of guns.

This concept is illustrated below. This imaginary model has two heads:

NSFW classification: general_nsfw, general_suggestive, general_not_nsfw_not_suggestive

Gun classification: gun_in_hand, animated_gun, gun_not_in_hand, no_gun

The confidence scores for each model head sum to 1.

When submitting a video to be processed, Hive’s backend splits the video into frames, runs the model on each frame, then recombines the results into an aggregated response for the entire video. The video output for a classifier is similar to a list of classification output objects, but with multiple timestamps.

Visual Content Moderation

Note: Older versions of the API might not perfectly match the outline below. Please reach out to [email protected] if you would like to access the latest content moderation classes.

Sexual

NSFW Head:

• general_nsfw - genitalia, sexual activity, nudity, buttocks, sex toys
• general_suggestive - shirtless men, underwear / swimwear, sexually suggestive poses without genitalia
• general_not_nsfw_not_suggestive - none of the above, clean

Female Underwear Head:

• yes_female_underwear - lingerie, bras, panties
• no_female_underwear

Male Underwear Head:

• yes_male_underwear - fruit-of-the-loom, boxers
• no_male_underwear

Sex Toy Head:

• yes_sex_toy - dildos, certain lingerie
• no_sex_toy

Female Nudity Head:

• yes_female_nudity - breasts or female genitalia
• no_female_nudity

Male Nudity Head:

• yes_male_nudity - male genitalia
• no_male_nudity

Female Swimwear Head:

• yes_female_swimwear - bikinis, one-pieces, not underwear
• no_female_swimwear

Shirtless Male Head:

• yes_male_shirtless - shirtless below mid-chest
• no_male_shirtless

Violence

Gun Head:

• gun_in_hand - person holding rifle, handgun
• gun_not_in_hand - rifle, handgun, not in hand
• animated_gun - gun in games, cartoons, etc. can be in-hand or not.
• no_gun

Knife Head:

• knife_in_hand - person holding knife, sword, machete, razor blade
• knife_not_in_hand - knife, sword, machete, razor blade, not in hand
• culinary_knife_in_hand - knife being used for preparing food
• no_knife

Blood Head:

• very_bloody - gore, visible bleeding, self-cutting
• a_little_bloody - fresh cuts / scrapes, light bleeding
• no_blood - minor scabs, scars, acne, etc. are not considered ‘blood’ by model
• other_blood - animated blood, fake blood, animal blood such as game dressing

Drugs

Pill Head: (beta)

• yes_pills - pills and / or drug powders
• no_pills - no pills and / or drug powders

Injectable Head: (beta)

• illicit_injectables - heroin and other illegal injectables
• medical_injectables - injectables for medical use
• no_injectables - no injectable drug paraphernalia

Smoking Head: (beta)

• yes_smoking - cigarettes, cigars, marijuana, vapes, or other smoking paraphernalia
• no_smoking - no cigars, marijuana, vapes, or other smoking paraphernalia

Hate

Nazi Head: (beta)

• yes_nazi - Nazi symbols
• no_nazi - absence of the above

Terrorist Head: (beta)

• yes_terrorist - ISIS flag
• no_terrorist - absence of the above

White Supremacy Head: (beta)

• yes_kkk - KKK symbols
• no_kkk - absence of the above

Middle Finger Head: (beta)

• yes_middle_finger - middle finger
• no_middle_finger - absence of the above

Other Attributes

Text Head: (beta)

• text - text is present somewhere on the image
• no_text - no text present in the image

Image Type Head: (beta)

• animated - the image is animated
• hybrid - the image is partially animated
• natural - the image has no animation

Choosing Thresholds

For each of the classes mentioned above, you will need to set thresholds to decide when to take action based on our model results. For optimum results, a proper threshold analysis on a natural distribution of your data is recommended (for more on this please contact Hive at the email below). Generally, a model confidence score >.95 is a good place to start to flag an image for any class of interest. For questions on best practices, please message your point of contact at Hive or send a message to [email protected] to contact our API team directly.