Deep Learning Models for Vision Tasks on iOS
Swift C++ Objective-C++ C Objective-C
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enVision.xcodeproj Fall 2016 Jan 6, 2017
enVision changed labels May 15, 2017
models changed labels May 15, 2017
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README.md

enVision

Deep Learning Models for Vision Tasks on iOS

sample

Usage

Download dependencies folder tf

Extract all archives in tf/models and tf/lib

Put tf folder in same directory level as enVision project folder

Build and Run

Press screen to change running model

Tap a data slot below to select, then tap a detection box to snap

Tap a data slot with two fingers to remove last snap

Press a data slot to clear

Models

YOLO:

https://arxiv.org/abs/1506.02640

sample2

YOLO 1 tiny (VOC): Best performance on basic classes

YOLO 1 small (VOC): Better accuracy for basic classes

YOLO 1.1 tiny (COCO): Fast on extended classes

YOLO 2 (COCO): Best accuracy on extended classes

YOLO detector + Jetpac feature extractor from snaps + kNN classifier with Euclidean distance

.

FaceNet:

https://arxiv.org/abs/1503.03832

sample3

Inception-Resnet-v1 (FaceScrub and CASIA-Webface)

Native iOS face detector + FaceNet feature extractor from snaps + kNN classifier with Euclidean distance

.

Inception:

https://arxiv.org/abs/1512.00567

Inception v3 (ImageNet)

Can run retrained models instead

.

Jetpac:

https://github.com/jetpacapp/DeepBeliefSDK

Jetpac network (ImageNet)

DeepBeliefSDK framework

.

License

MIT License

Owner: ID Labs L.L.C.

Original Contributor: Muhammad Hilal