Rocketknight1

# Assume labels is a possibly multidimensional array of categories / token indices

_, label_counts = np.unique(labels, axis=None, return_counts=True)  # Will flatten multidimensional arrays

# For multi-label classification you should normalize by the number of samples instead
label_frequencies = label_counts.astype(np.float) / np.sum(label_counts)

label_logprobs = np.log(label_frequencies)

# Now you just need to assign the values in label_logprobs to your bias vector!

ariG23498

alpercalisir

import pandas as pd
import numpy as np
from lxml import etree
import xmlAnnotation.etree.cElementTree as ET

fields = ['NAME_ID', 'XMIN', 'YMIN', 'W', 'H', 'XMAX', 'YMAX']
df = pd.read_csv('loose_bb_test.csv', usecols=fields)


# Change the name of the file.

stefanonardo

# MIT License
#
# Copyright (c) 2018 Stefano Nardo https://gist.github.com/stefanonardo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

douglasrizzo

How to train your own object detection models using the TensorFlow Object Detection API (2020 Update)

This started as a summary of this nice tutorial, but has since then become its own thing.

• Prerequisites
• Annotating images and serializing the dataset
• Choosing a neural network and preparing the training pipeline
• Training the network

soply

import matplotlib.pyplot as plt
import numpy as np

def show_images(images, cols = 1, titles = None):
    """Display a list of images in a single figure with matplotlib.
    
    Parameters
    ---------
    images: List of np.arrays compatible with plt.imshow.