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Deep learning and AI

AI

ML Machine Learning releases

01 - Learn Deep Learning
02 - Resources

Image Recognition (10 Videos):

Deep / Machine learning resources:

External Links:

03 - Clarifai - Artificial Intelligence with a Vision

Clarifai Reference:

Forum and Help:

Step by step how to install python, clarify and run a demo code

  1. Download & Install Python - Just download and install 2.X
  2. Windows
    • Add to system path:
      • c:\python27\
      • c:\python27\scripts\
  3. Run example code of Python
    • print "Hello World"
  4. Open command prompt and run the following:
    • pip install clarifai==2.0.28
  5. Install guide for clarifai
  6. After a successful install without error messages, do the following:
    1. change directory to c:\python27\scripts
    2. At command prompt run the following: python clarifai config
  7. Provide clarifai_app_id and clarifai app_secret as follows:
    • Under your clarifai account use or create new app. Find HIDE LEGACY AUTHORIZATION
    • The app ID and Secret will be there.
  8. Login to your clarifai account
  9. Select PREDICT from the following page:
  10. Copy and paste the following to a python file:
from clarifai.rest import ClarifaiApp

app = ClarifaiApp("{client_id}", "{client_secret}")

# get the general model
model = app.models.get("general-v1.3")

# predict with the model
model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg')
04 - TensorFlow
05 - Caffe

 

06 - SciKit
07 - Useful tools
08 - VGG
09 - Datasets
10 - Ted video about Deep learning / Coco / Yolo / Darknet

 

11 - Darknet: Open Source Neural Networks in C

Steps to install:

  • Install Ubuntu 16.04 lts
  • Open terminal and install GIT:
    • sudo apt-get update
    • sudo apt-get install git
  • Clone Darknet repo
    • git clone https://github.com/pjreddie/darknet.git
    • cd darknet
    • make
    • ./darknet
    • You suppose too get hte following output: usage: ./darknet
  • Install OpenCV
    • sudo apt-get install libopencv-dev python-opencv
    • Edit make file and change to OPENCV=1
    • Make again
    • Test by the following command line:
      • ./darknet imtest data/eagle.jpg

How to test Darknet?

  • At terminal, at darknet directory write:
    • ./darknet
      • Result: usage: ./darknet
  • How to test OpenCV?
    • Open terminal and paste:
      • ./darknet imtest data/eagle.jpg
      • A window with eagle picture will appear.
  • How to test darknet predict and classify?
    • Download Extractions.weights. 
    • Open terminal and paste:
      • ./darknet classifier predict cfg/imagenet1k.data cfg/extraction.cfg extraction.weights data/eagle.jpg
        • Result: classification of image.
  • How to test Yolo?
    • Download Yolo.weights
    • Open terminal and paste:
      • ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg
        • Result: image with classification

Darknet Website

  • Link: https://pjreddie.com/darknet/yolo/
  • Live video: 
    • ./darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights
  • Video from file:
    • ./darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights 
  • Changing The Detection Threshold
    • ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg -thresh 0

Docker

  • Pull darknet
    • sudo docker pull loretoparisi/darknet
  • Test Docker darknet:
    • run darknet
      • docker run --rm -it --name darknet loretoparisi/darknet bash
      • cd darknet
      • ./darknet detector test cfg/coco.data cfg/yolo.cfg /root/yolo.weights data/dog.jpg