| def get_classification_report(y_test, y_pred): | |
| '''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format''' | |
| from sklearn import metrics | |
| report = metrics.classification_report(y_test, y_pred, output_dict=True) | |
| df_classification_report = pd.DataFrame(report).transpose() | |
| df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False) | |
| return df_classification_report |
| import numpy as np | |
| def non_max_suppression(boxes, scores, threshold): | |
| assert boxes.shape[0] == scores.shape[0] | |
| # bottom-left origin | |
| ys1 = boxes[:, 0] | |
| xs1 = boxes[:, 1] | |
| # top-right target | |
| ys2 = boxes[:, 2] |
| FROM python:3.4 | |
| ADD . /app/ | |
| WORKDIR /app/ | |
| RUN pip install -r requirements.txt | |
| CMD ["echo", "hello"] |
| ## https://gist.github.com/pierriko/fd49abfa2fb3561f9c89 | |
| ## https://github.com/omgteam/Didi-competition-solution | |
| ## https://discussions.udacity.com/t/processing-velodyne-data-for-dataset-2/237084/7 | |
| # attach process: https://www.jetbrains.com/help/pycharm/2016.3/attaching-to-local-process.html | |
| import sys | |
| ##sys.path.insert(0,'/opt/ros/kinetic/lib/python2.7/dist-packages') | |
| ##sys.path.insert(0,'/usr/lib/python2.7/dist-packages') ## rospkg |
| """ | |
| Clean and simple Keras implementation of network architectures described in: | |
| - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf). | |
| - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf). | |
| Python 3. | |
| """ | |
| from keras import layers | |
| from keras import models |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| #Import both skimage and cv | |
| from skimage import transform as tf | |
| from skimage import io | |
| import cv2 | |
| import numpy as np | |
| from scipy import optimize | |
| import matplotlib.pyplot as plt | |
| # Could use either skimage or cv to read the image |
This is taken from here. http://stackoverflow.com/questions/67699/how-do-i-clone-all-remote-branches-with-git/72156#72156 Want this as a gist because I always come back to this posting.
First, clone a remote git repository and cd into it:
$ git clone git://example.com/myproject
$ cd myproject
Next, look at the local branches in your repository:
| // new | |
| /*M/////////////////////////////////////////////////////////////////////////////////////// | |
| // | |
| // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. | |
| // | |
| // By downloading, copying, installing or using the software you agree to this license. | |
| // If you do not agree to this license, do not download, install, | |
| // copy or use the software. | |
| // | |
| // |