caffemodel: age_net.caffemodel
caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
""" | |
Installation script for the vlfeat module | |
""" | |
import sys, os | |
from distutils.core import Extension, setup | |
from distutils.errors import DistutilsFileError |
""" | |
A deep neural network with or w/o dropout in one file. | |
License: Do What The Fuck You Want to Public License http://www.wtfpl.net/ | |
""" | |
import numpy, theano, sys, math | |
from theano import tensor as T | |
from theano import shared | |
from theano.tensor.shared_randomstreams import RandomStreams |
""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
--[[ | |
Efficient LSTM in Torch using nngraph library. This code was optimized | |
by Justin Johnson (@jcjohnson) based on the trick of batching up the | |
LSTM GEMMs, as also seen in my efficient Python LSTM gist. | |
--]] | |
function LSTM.fast_lstm(input_size, rnn_size) | |
local x = nn.Identity()() | |
local prev_c = nn.Identity()() | |
local prev_h = nn.Identity()() |
caffemodel: age_net.caffemodel
caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel
Convolutional neural networks for emotion classification from facial images as described in the following work:
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
If you find our models useful, please add suitable reference to our paper in your work.
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs