Computer Vision Model

In the prevision.io platform, you can train models using images for :

• regression

• classification ( either simple one with 2 class or multiclassification with more than 2 class )

And a Special one :

• image detection

When selecting an image usecase, you can train a classification, a multiclassification, a regression or an object detector

Note that your models can be trained on mixed dataset : you can have a dataset with tabular datas, columns of text, categorical datas and linear one.

For example, this dataset is valid :

A dataset with mixed data

id	path_image	description	screenshot_day	nbclick	cat	TARGET
1	games/action/screenshot_1.png	a brutal top-down […]	2020-03-12	4555	ACTION	offensive
2	games/action/screenshot_2.png	players compete […]	2020-05-12	12298	ACTION	neutral
3	games/simulation/screenshot_1.png	An intern in an […]	2020-06-04	10024	SIMULATION	offensive

Setup

When training an image use case, you always need 2 assets :

• a csv dataset with path to the images and differents info ( and the target )

• a zip of images uploaded in your image folder storage space

The csv dataset is a classic dataset imported with connectors or uploaded via interface while the zip is a zip file of image set up on your local environment and uploaded to your image folder storage.

More over, for Object Detector traning, you need to draw bounding box onto your image and export them to Pascal Voc format.

Regression

A regression on images is a model that predict a number from an image, for example number of click from a thumbnail or a screenshot.

Classification

A classification is a model that predict class of an image. There could be 2 class or more. For example “smiling”, “crying” or “thinking” from the image of a face.

A classification use case with Image path input

Object detection

An object-detection use case is a model that from an image return zero, one or more bounding box with label.

An image with its training bounding box ( blue ), the predicted bounding box ( orange ) with predicted label and probability

Image Path

When there are image in a dataset, the path to each image from the root of the image folder should be put in a column and this column muste then be selected as the “Image path” column.

For example if your folder of image has this architecture :

You should zip it from the root :

Upload the zip to your image folder Storage

and your dataset should have a column with the following info

path info are from the root

path	class	otherinformation
gruyere/image_20210806_110725_16780.jpg	gruyere	yellow
gruyere/image_20210806_105914_1559.jpg	gruyere	yellow
mimolette/image_20210806_105914_1553.jpg	mimolette	orange
mimolette/image_20210806_105914_1558.jpg	mimolette	orange
mimolette/image_20210806_105914_1551.jpg	mimolette	orange
neufchatel/image_20210806_110935_20864.jpg	neufchatel	white

When setting the experiment, this path from the zip root should be mapped to the ‘image path’ input.

Label or class

For classification, Multiclassification and Object Detection, you must set a column with the name of a class, or label

A label column

path	label	otherinformation
gruyere/image_20210806_110725_16780.jpg	gruyere	yellow
gruyere/image_20210806_105914_1559.jpg	gruyere	yellow
mimolette/image_20210806_105914_1553.jpg	mimolette	orange
mimolette/image_20210806_105914_1558.jpg	mimolette	orange
mimolette/image_20210806_105914_1551.jpg	mimolette	orange
neufchatel/image_20210806_110935_20864.jpg	neufchatel	white

You can call it anything you want but when setting your experiment, you need to select it in the ‘class’ input selector

Bounding box

If you want to train a model to detect object on a image, you must run an Object Detection training and you must provide a dataset with bounding box for training

This bounding box is described in a dataset in csv format that can be imported or uploaded to your data section. Bounding box always use 4 columns to be described but there are two format for bounding box :

• Pascal Voc bounding box : x-top left, y-top left,x-bottom right, y-bottom right

• Yolov bounding box : x-top left, y-top left, width, height ( of the bounding box )

Prevision Platform use the Pascal Voc Bounding box format in csv. So to describe a bounding box , you must provide 4 more columns and map them in the input interface when setting your object detection experiment :

A complete object detector dataset

path	category	xleft	ytop	xright	ybottom	imgname	labels
image_20210806_105845_425.jpg	patemolleacroutelavee	228	634	846	984	image_20210806_105845_425.jpg	vieuxboulogne
image_20210806_105845_428.jpg	patemolleacroutelavee	83	99	302	329	image_20210806_105845_428.jpg	vieuxboulogne
image_20210806_105845_434.jpg	patemolleacroutelavee	0	4	188	145	image_20210806_105845_434.jpg	vieuxboulogne
image_20210806_105846_446.jpg	patemolleacroutelavee	36	8	244	215	image_20210806_105846_446.jpg	vieuxboulogne

Note that for object detection, there can be several object in one image. So your dataset may have many row with same image paht but different bounding box and label :

Several bounding box on one image

path	xleft	ytop	xright	ybottom	labels
image_20210806_105845_425.jpg	228	634	846	984	vieuxboulogne
image_20210806_105845_425.jpg	455	877	565	984	mimolette
image_20210806_105845_425.jpg	28	64	754	800	mimolette
image_20210806_105845_425.jpg	22	34	600	900	cabecou

Object Segmentation

Prevision.io does not provide Object Segmentation models.

Launch train

Once you had mapped each of the input to the corresponding columns, and given a name to your experiment, you can click on the train button and the training will start.

For Regression, Classification and multiclassification, you will find the standard metrics and chart. For Object Detection, the metrics is Mape and represent the accuracy of the bounding box.