algorithmiahq / DeepFashion / 1.5.1
README.md
This algorithm detects clothing items in images; it returns a list of discovered clothing articles as well as annotating the input image with bounding boxes for each found article.
Note: We've recently made major upgrades to this algorithm, including changing the underlying model to improve our results. Give it another shot!
Model comparison Table
model | Average Compute time (s) | Mean Average Precision (mAP) |
---|---|---|
small | 8.2 | 23 |
mid | 10.9 | 28 |
large | 17.3 | 32 |
Class list
Curious about what classes this algorithm has been trained to detect? Take a look below:
'top handle bag'
't shirt'
'jewelry'
'boots'
'sunglasses'
'jeans'
'sweater'
'tank top'
'skirt'
'sandals'
'leggings'
'button down shirt'
'pants casual'
'heels pumps or wedges'
'lingerie'
'blouse'
'lightweight jacket'
'casual dress'
'winter jacket'
'formal dress'
'watches'
'hat'
'vest'
'sneakers'
'shoulder bag'
'flats'
'overall'
'sweatpants'
'shorts'
'rompers'
'pants suit formal'
'glasses'
'clutches'
'socks'
'backpack or messenger bag'
'jumpsuit'
'running shoes'
'blazer'
'tunic'
'hosiery'
'denim jacket'
'belts'
'leather jacket'
'trenchcoat'
'headwrap'
'sweater dress'
'sweatshirt'
'gloves'
'underwear'
I/O
Json input variant
Input
{
"image":String,
"output":String,
"threshold": Float,
"mode":String,
"tags_only":String
}
- image - (required) - an input image as either a url, data connector uri (data://, s3://, etc) or a base 64 encoded string.
- output - (optional) - the output data connector path to where you want to save the thumbnail to. defaults to
data://.algo/temp/UUID.png
- threshold - (optional) - the minimum confidence for a label to be drawn / returned in the tag list. defaults to
0.65
- tags_only - (optional) - set this to true if you don't want the annotated image output. defaults to
'false'
- mode - (optional) - defines the
mode
for the algorithm, mode can besmall
,mid
, orlarge
. A break down on performance is shown above.
Simple input variant
Input
input: String/Byte[]
- input - (required) - an input image as either a url, data connector uri (data://, s3://, etc), base 64 encoded string, or a binary array.
Output
{
"output":String,
"articles":[
{
"bounding_box":{
"x0":Float,
"x1":Float,
"y0":Float,
"y1":Float
},
"article_name":String,
"confidence":Double
},
...
]
}
- output - If tags_only is
false
, then this key contains the url to the annotated image. - articles - the list of discovered articles.
- bounding box - the rectangular coordinates of the most relevent bounding box, defined as (x0,y0), (x1,y1).
- article_name - the name of the discovered article.
- confidence - the algorithm's confidence that this article exists in the image.
Example
Input
{
"image":"http://i.imgur.com/GtvDM8X.jpg",
"model":"mid",
"output":"data://.algo/perm/suit.png"
}
Output
"articles": [
{
"article_name": "button down shirt",
"bounding_box": {
"x0": 431,
"x1": 905,
"y0": 267,
"y1": 702
},
"confidence": 0.9515272378921508
},
{
"article_name": "hat",
"bounding_box": {
"x0": 517,
"x1": 807,
"y0": 80,
"y1": 215
},
"confidence": 0.9933492541313172
}
],
"output": "data://.algo/perm/suit.png"
}
Credits
This algorithm is a machine learning model based on the faster-rcnn project,which was inspired by the Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks paper.
All sample images courtesy of the wikimedia foundation
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