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magnetron.artificial-intelligence-2.0.mincloud.proxia--ASTRAL-MIND

THE ABC 123 GROUP ™:

🌐 GENERAL CONSULTING ABC 123 BY OSAROPRIME ™.

🌐 ABC 123 USA ™

🌐 ABC 123 DESYGN ™

🌐 ABC 123 FILMS ™

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                 🌐 MAGENTRON ™ 🌐

🌐 ARTIFICIAL INTELLIGENCE 2.0 ™ : SENTENCE DESCRIPTION PROXIA (DENSE CAPTIONING)

🌐 ASTRAL BODY MINDCLOUD:

🌐 PRANIC BODY MINDCLOUD:

🌐 INSTINCTIVE MIND MINDCLOUD:

🌐 ASTRAL MIND MINDCLOUD: ✅

🌐 PRANIC MIND MINDCLOUD:

*️⃣📶🤖

REQUIREMENTS:

[*] Software Requirements: Python, Tensor Flow

[*] HARDWARE REQUIREMENTS: fast TPU/GPU.

[*] DEPENDENCIES:

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Dense captioning is a computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. This can be used to Generate sentence description of images (with LOGIC).

🌐 NOTE: Detected OBJECTS and OBJECT PARTS are oftn associated with noun text SYMBOLS. (SEE 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗖𝗘 𝟮.𝟬 ™ DOCUMENTATION).

🌐 NOTE: INTERACTIONS between OBJECTS are often associated with verb text SYMBOLS. (SEE 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗖𝗘 𝟮.𝟬 ™ DOCUMENTATION).

e.g This ASTRAL MIND MINDCLOUD PROXIA can be used to process INFORMATION from the real world via the eye cameras and then send information about the IMAGE to the IMAGINATION proxia on ASTRAL MINDCLOUD for the robot to IMAGINE it in different scenarios to better understand what it is and how people see it.

e.g This PROXIA can be used to classify SITUATIONS with LOGIC (in THE ARTIFICIAL INTELLIGENCE 2.0 ™ FRAMEWORK).

Prerequisite reading:

🌐 ARTIFICIAL INTELLIGENCE PRIMER ™: https://www.facebook.com/artificialintelligenceprimer

🌐 ARTIFICIAL INTELLIGENCE 2.0 ™ DOCUMENTATION: https://www.facebook.com/aibyabc123/

🌐 MEMBER'S CLUB ™ DOCUMENTATION - https://www.facebook.com/abc123membersclub/

👑 INCLUDED STICKERS/SIGN:

FIND STICKERS HERE: https://bit.ly/3B8D3lE

  • PROMOTIONAL MATERIAL FOR 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™. (CUSTOM GRAPHICS BY 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗗𝗘𝗦𝗬𝗚𝗡 ™/𝗢𝗦𝗔𝗥𝗢 𝗛𝗔𝗥𝗥𝗜𝗢𝗧𝗧). THE 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™ SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ FOR 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™. 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™.

*️⃣📶🤖

  • PROMOTIONAL MATERIAL FOR 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗖𝗘 𝟮.𝟬 ™. (CUSTOM GRAPHICS BY 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗗𝗘𝗦𝗬𝗚𝗡 ™/𝗢𝗦𝗔𝗥𝗢 𝗛𝗔𝗥𝗥𝗜𝗢𝗧𝗧) THE 𝗗𝗥𝗔𝗚𝗢𝗡 & 𝗖𝗥𝗢𝗪𝗡 👑 SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ ASSOCIATED WITH TECHNOLOGY. 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™.

You must display the included stickers/signs (so that it is clearly visible) if you are working with MAGNETRON ™ TECHNOLOGY for the purposes of determining whether you want to purchase a technology license or not. This includes but is not limited to public technology displays, trade shows, technology expos, media appearances, Investor events, Computers (exterior), MINDCLOUD STORAGE (e.g server room doors, render farm room doors) etc.

🌐 NOTE: REMEMBER 1 FRAME OF VIDEO (FROM EYE CAMERAS) CAN BE SAVED AS AN IMAGE FILE.

Densecap-tensorflow

Implementation of CVPR2017 paper: Dense captioning with joint inference and visual context by Linjie Yang, Kevin Tang, Jianchao Yang, Li-Jia Li

WITH CHANGES:

  1. Borrow the idea of Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling, and tied word vectors and word classfiers during captioning.
  2. Initialize Word Vectors and Word Classifers with pre-trained glove word vectors with dimensions of 300.
  3. Change the backbone of the framework to ResNet-50.
  4. Add Beam Search and Length Normalization in test mode.
  5. Add "Limit_RAM" mode when praparing training date since my computer only has RAM with 8G.

Note

Update 2017.12.31

  • After 500k iterations of training with configurations of original paper (except for the weights tying of wordvec and classifiers), it achieves mAP 8.296.

Update 2017.12.20

  • After 1 epoch(80000 iters) of training with randomly initialized word vectors(512d), it achieves mAP 6.509.
  • After 1 epoch(75000) of training with pre-trianed glove word vectors(300d), it got mAP 5.5 nearly.
  • The complete training process will take almost 10 days with the computation I have access to, and I just trained 1 epoch to varify the framework for now.
  • The scripts should be compatible with both python 2.X and 3.X. Although I built it under python 2.7.
  • Tested on Ubuntu 16.04, tensorflow 1.4, CUDA 8.0 and cudnn 6.0, with GPU Nvidia gtx 1060(LOL...).

Dependencies

To install required python modules by:

pip install -r lib/requirements.txt

For evaluation, one also need:

To install java runtime by:

sudo apt-get install openjdk-8-jre

Preparing data

Download

Website of Visual Genome Dataset

  • Make a new directory VG wherever you like.
  • Download images Part1 and Part2, extract all (two parts) to directory VG/images
  • Download image meta data, extract to directory VG/1.2 or VG/1.0 according to the version you download.
  • Download region descriptions, extract to directory VG/1.2 or VG/1.0 accordingly.
  • For the following process, we will refer the absolute path of directory VG as raw_data_path, e.g. /home/user/git/VG.

Unlimit RAM

If one has RAM more than 16G, then you can preprocessing dataset with following command.

$ cd $ROOT/lib
$ python preprocess.py --version [version] --path [raw_data_path] \
        --output_dir [dir] --max_words [max_len]

Limit RAM (Less than 16G)

If one has RAM less than 16G.

  • Firstly, setting up the data path in info/read_regions.py accordingly, and run the script with python. Then it will dump regions in REGION_JSON directory. It will take time to process more than 100k images, so be patient.
$ cd $ROOT/info
$ python read_regions --version [version] --vg_path [raw_data_path]
  • In lib/preprocess.py, set up data path accordingly. After running the file, it will dump gt_regions of every image respectively to OUTPUT_DIR as directory.
$ cd $ROOT/lib
$ python preprocess.py --version [version] --path [raw_data_path] \
        --output_dir [dir] --max_words [max_len] --limit_ram

Compile local libs

$ cd root/lib
$ make

Train

Add or modify configurations in root/scripts/dense_cap_config.yml, refer to 'lib/config.py' for more configuration details.

$ cd $ROOT
$ bash scripts/dense_cap_train.sh [dataset] [net] [ckpt_to_init] [data_dir] [step]

Parameters:

  • dataset: visual_genome_1.2 or visual_genome_1.0.
  • net: res50, res101
  • ckpt_to_init: pretrained model to be initialized with. Refer to tf_faster_rcnn for more init weight details.
  • data_dir: the data directory where you save the outputs after prepare data.
  • step: for continue training.
    • step 1: fix convnet weights
    • stpe 2: finetune convnets weights
    • step 3: add context fusion, but fix convnets weights
    • step 4: finetune the whole model.

Demo

Create a directory data/demo

$ mkdir $ROOT/data/demo

Then put the images to be tested in the directory.

Download pretrained model (iters 500k) by Google Drive or Jbox. Then create a "output" directory under $ROOT

$ mkdir $ROOT/output

Extract the downloaded "ckpt.zip" to directory $ROOT/output. And run

$ cd $ROOT
$ bash scripts/dense_cap_demo.sh ./output/ckpt ./output/ckpt/vocabulary.txt

or run

$ bash scripts/dense_cap_demo.sh [ckpt_path] [vocab_path]

for your customized checkpoint directory.

It will create html files in $ROOT/demo, just click it. Or you can use the web-based visualizer created by karpathy by

$ cd $ROOT/vis
$ python -m SimpleHTTPServer 8181

Then point your web brower to http://localhost:8181/view_results.html.

TODO:

  • preprocessing dataset.
  • roi_data_layer & get data well prepared for feeding.
  • proposal layer
  • sentense data layer
  • embedding layer
  • get loc loss and caption loss
  • overfit a mini-batch
  • context fusion
  • add experiment result.

References