Live Updates: COVID-19 Cases
  • World 21,355,685
    World
    Confirmed: 21,355,685
    Active: 6,443,009
    Recovered: 14,149,309
    Death: 763,367
  • USA 5,476,266
    USA
    Confirmed: 5,476,266
    Active: 2,429,584
    Recovered: 2,875,147
    Death: 171,535
  • Brazil 3,278,895
    Brazil
    Confirmed: 3,278,895
    Active: 788,022
    Recovered: 2,384,302
    Death: 106,571
  • India 2,525,222
    India
    Confirmed: 2,525,222
    Active: 668,532
    Recovered: 1,807,556
    Death: 49,134
  • Russia 912,823
    Russia
    Confirmed: 912,823
    Active: 174,361
    Recovered: 722,964
    Death: 15,498
  • South Africa 579,140
    South Africa
    Confirmed: 579,140
    Active: 105,850
    Recovered: 461,734
    Death: 11,556
  • Peru 516,296
    Peru
    Confirmed: 516,296
    Active: 136,208
    Recovered: 354,232
    Death: 25,856
  • Mexico 511,369
    Mexico
    Confirmed: 511,369
    Active: 109,808
    Recovered: 345,653
    Death: 55,908
  • Chile 382,111
    Chile
    Confirmed: 382,111
    Active: 16,734
    Recovered: 355,037
    Death: 10,340
  • Spain 358,843
    Spain
    Confirmed: 358,843
    Active: 330,226
    Recovered: ?
    Death: 28,617
  • Iran 338,825
    Iran
    Confirmed: 338,825
    Active: 25,683
    Recovered: 293,811
    Death: 19,331
  • UK 316,367
    UK
    Confirmed: 316,367
    Active: 275,009
    Recovered: ?
    Death: 41,358
  • Saudi Arabia 295,902
    Saudi Arabia
    Confirmed: 295,902
    Active: 29,605
    Recovered: 262,959
    Death: 3,338
  • Pakistan 288,047
    Pakistan
    Confirmed: 288,047
    Active: 16,261
    Recovered: 265,624
    Death: 6,162
  • Bangladesh 271,881
    Bangladesh
    Confirmed: 271,881
    Active: 111,667
    Recovered: 156,623
    Death: 3,591
  • Italy 252,809
    Italy
    Confirmed: 252,809
    Active: 14,249
    Recovered: 203,326
    Death: 35,234
  • Turkey 246,861
    Turkey
    Confirmed: 246,861
    Active: 11,947
    Recovered: 228,980
    Death: 5,934
  • Germany 223,774
    Germany
    Confirmed: 223,774
    Active: 11,935
    Recovered: 202,550
    Death: 9,289
  • France 212,211
    France
    Confirmed: 212,211
    Active: 97,957
    Recovered: 83,848
    Death: 30,406
  • Canada 121,652
    Canada
    Confirmed: 121,652
    Active: 4,690
    Recovered: 107,942
    Death: 9,020
  • China 84,808
    China
    Confirmed: 84,808
    Active: 655
    Recovered: 79,519
    Death: 4,634
  • Netherlands 61,840
    Netherlands
    Confirmed: 61,840
    Active: 55,673
    Recovered: ?
    Death: 6,167
  • Australia 23,035
    Australia
    Confirmed: 23,035
    Active: 9,301
    Recovered: 13,355
    Death: 379
  • S. Korea 15,039
    S. Korea
    Confirmed: 15,039
    Active: 833
    Recovered: 13,901
    Death: 305
  • New Zealand 1,609
    New Zealand
    Confirmed: 1,609
    Active: 56
    Recovered: 1,531
    Death: 22

Apple Discusses Vision Framework and Face Detection in Machine Learning Journal

Author at TechGenyz Apple
Apple Face Detection

Today, a new entry in Apple’s Machine Learning Journal was published, where face detection and related Vision framework were discussed for the developers to use for apps in macOS, iOS, and tvOS.

The entry is titled “An On-device Deep Neural Network for Face Detection,” and it explores the barriers against Vision to work. It also covers privacy maintenance ‘by running detection’ locally, and not via cloud servers.

An excerpt from the paper by Computer Vision Machine Learning Team of Apple reads, “The deep-learning models need to be shipped as part of the operating system, taking up valuable NAND storage space. They also need to be loaded into RAM and require significant computational time on the GPU and/or CPU.”

The team further supports on-device computation over cloud-based services, during the process of system resource share, while other apps are running. At the same time, the team writes about a high-level efficiency requirement for the computation for processing huge Photos library in – short time, low thermal increase, and low power usage.

For breaking the barriers, Apple optimized the framework to ‘fully leverage’ CPUs and GPUs with the help of BNNS (Basic Neural Network Subroutines) and Metal graphics of the brand. It optimized the memory usage as well for image loading and caching and network inference.

It is recently noted that Apple is heavily investing in machine learning. They came up with executing a ‘Neural Engine,’ dedicated for the A11 Bionic processor in iPhone X and 8. Furthermore, CEO Tim Cook said earlier this year that machine learning is an indispensable asset for the self-driving car platform of Apple which presently in testing on the roads of California.

How do you like the new developments in Apple’s contributions towards machine learning? Do you think you could have anything to add to the paper? State your opinions in comments and stay with us for more tech updates.

Career

Subscribe