12500 cat names for your kitten
250+ Badass Cat Names for Your Ferocious Feline
If your kitty is one tough cookie, one of these badass cat names will help her feel more like the lioness she is inside.
Badass cats of all shapes, sizes, and sassiness have a special place in our hearts here at Daily Paws. Whether he’s a daredevil Devon rex, a wild and free ocicat, or anything in between, these badass cat names are sure to get a purr of approval.
Badass Female Cat Names
Looking for a badass name for your sassy girl cat? One of these cool monikers is sure to have your rebellious feline friend feeling proud and meowing loud.
- Megan (Markle, Thee Stallion, Fox, Rapinoe … the list is endless)
- Barbra Streisand (Babs for short)
- Selina Kyle (a.k.a. Catwoman)
- Diana (Wonder Woman)
- Mrs. Business
Badass Male Cat Names
Your boy cat is already a badass dude without one of these names, but we like to think you can never go overboard with pawesomeness.
- Josey (Wales)
- Snake (Plissken)
- Mr. Business
- Bruce Wayne
- Terminator (or Furrminator?)
- Hans Gruber
- Inigo Montoya
- Don Vito
- Big Papa (the perfect name for a Maine Coon)
Badass Names for Black Cats
Contrary to superstitions, black cats make great pets! Some are known for being daredevils like this cat who jumped five stories to the ground and was fine. When they say cats have nine lives, they must be referring to the badass cats on this list.
Badass Orange Cat Names
Make sure you pick the perfect name to suit your daredevil cat whose orange colorations call to mind the drama of flames and lava. Or your orange cat may look and act like a tiny tiger, making one of these wildlife names the perfect choice.
Badass Grey Cat Names
Mystical silver felines are cool in more elevated, mysterious ways than cats named Butch or Ranger. They prefer to keep it on the down low and then surprise you with the extent of their badass-ery once you form an alliance.
- Dark Moon
Badass Cat Names Inspired by the All-Stars of the Marvel Cinematic Universe
Paws up if you know that Marvel superheroes are the most badass characters out there (sorry DC). We’re sure your cat would agree.
- Dora Milaje
- Marvel (or Mar-Vell)
- Doctor Strange
- Black Widow
- Iron Man
- War Machine
Badass Names for Cats Based on Some of the Coolest Fantasy Characters of All Time
From Game of Thrones and Lord of the Rings to more niche fantasy realms, these tough names are fitting for a cat whose imagination is larger than life.
- Jack Sparrow
- King Kong
Badass Cat Names Inspired by Our Favorite Athletes
If you have a cat that is the best at climbing, leaping, knocking stuff over, and is a whiz at catching mice, naming them after one of these GOATs just makes sense.
- Simone Biles
- Kobe Bryant
- Cristiano Ronaldo
- Serena Williams
- Muhammad Ali
- Michael Phelps
- Lionel Messi
- Marion Jones
- Rafael Nadal
- Yao Ming
- Apolo Anton Ohno
- Sugar Ray Leonard
- Tom Brady
- Shaun White
- Mia Hamm
- Misty May
- Danica Patrick
- Michelle Kwan
- Dale Earnhardt
- Jackie Robinson
Badass Disney Cat Names
It’s usually the villains that get the badass ruffian reputation in our favorite Disney films, but sometimes the heroes are just as strong and rugged.
Tough Cat Names for Badass Historical People Who Changed the World
Your cat can only hope to live up to a name fit for one of these legends. (Don’t worry, every cat is a superstar in our book.)
- Joan of Arc
- Ruth Bader Ginsburg
- Malcom X
- John Lewis
- Greta Thunberg
- Jane Goodall
- Dorothy Vaughan
- Leonardo da Vinci
- Albert Einstein
- Vincent van Gogh
Badass Cat Names from Star Wars
«Badass, your cat is.» -Yoda
- Luke Skywalker
- Han Solo
- Princess Leia
- Darth Vader
- Baby Yoda
- Darth Maul (fitting for certain cats)
- Count Dooku
- Jyn Erso
Badass Harry Potter Cat Names
If we could conjure a Patronus, it would definitely be a cat.
Badass Cat Names Inspired by Mythology
Legend has it that cats were sacred animals in mythological lore across countries and cultures, from the Norsemen to the Greeks to the Egyptians.
Badass Cat Names from Our Favorite Musical Artists
These queens are maybe some of the toughest cookies alive. If your cat likes listening to music, turn up a chart topper by one of these musicians and see which one makes her purr the loudest.
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Indira Gandhi International Airport (DEL/VIDP)
With the economy of India going from strength to strength it is no surprise to see a major expansion project underway at
New terminal buildings, expansion and improvement, new runway
New terminal buildings, expansion and improvement, new runway
2006 (first phase), there will be three phases
2008-2010 (DIAL has a 35 year concession to operate the airport)
Delhi International Airport Ltd, GMR Group, Fraport AG, Eraman Malaysia, India Development Fund, Airports Authority of India, Indian Government PPP initiative
Lead Contractors, Designers, Architects and Engineers
HOK, Mott McDonald, Larsen and Toubro, Meinhardt Engineering, Airbiz, Parsons Brinckerhoff International Inc, Woodhead
21.44 million in 2006-2007, 46 million per year by 2015 and 80 million per year by 2025
Indira Gandhi International (IGI) Airport, which was named after one of India’s most well known prime ministers, serves the city of Delhi which is about ten miles (16km) away. The airport, covering a 1,900-acre site, has two runways 10/28 (12,500ft 3,810m) and also an auxiliary runway 09/27 (9,229ft 2,813m). The main runway 10/28 is also one of the few runways in Asia that is equipped with a CAT III-B instrument landing system allowing landing in visibility as low as 50m.
The airport, which is the second busiest in India hosted 20.44 million passengers in 2006–2007 (domestic and international) and currently has six terminals including: domestic terminals 1A, 1B and 1D, the international terminal two, the Haj terminal and international cargo terminal.
Delhi International Airport operator
The airport is operated by Delhi International Airport Ltd (DIAL) which is a consortium led by an Indian developer, GMR Group, and including Fraport AG, Eraman Malaysia, India Development Fund and Airports Authority of India.
The expansion project is being carried out under the Public Private Partnership Initiative of the government of India. DIAL was awarded the modernisation and restructuring contract for the airport in January 2006 after submitting the winning bid. DIAL have developed a master plan for development of the airport over the next 30 years.
In addition they have a concession to run the airport for 35 years with an option to extend the arrangement for a further 30 years. The whole renovation and expansion programme is expected to cost $2bn.
Indira Gandhi International Airport modernisation and expansion
DIAL took over IGI Airport in May 2006 and immediately undertook a renovation programme for the existing 1A and 1B terminals as a stop gap measure to give them a modern look (this included seating, lighting, decoration inside and out, air conditioning and toilets). Terminal 1D was opened in February 2009, increasing the departure capacity of the airport to 10 million passengers a year.
Renovation of terminal two was completed in July 2008. Modernisation works at the terminal included a new three-level in-line baggage-handling system, 22 additional check-in booths and a new customs and immigration area. The terminal now has 100 check-in counters and 52 immigration counters in the departure area and 48 counters in the arrival area.
The main emphasis is being put on the major expansion programme currently underway at the airport (phase one). This will include the construction of a new terminal three (T3) which will require an investment of over $700m and be ready in time for India to host the Commonwealth Games in 2010.
The new terminal will give the airport a capacity of 37 million passengers a year.
Also a new code-F parallel runway (4,430m x 60m) was opened for commercial operations in September 2008. The runway is one of the longest in Asia and has CAT IIIB ILS capability.
Terminal three (330m by 250m) (will handle 100% of international and 50% of domestic traffic when it is opened), which will have a floor space of 480,000m² was designed by HOK in consultation with Mott McDonald and is being constructed by Larsen and Toubro, Meinhardt Engineering and Airbiz of Australia (airport planner) with project management by Parsons Brinckerhoff International Inc.
The interior and retail design is being carried out by Woodhead of Australia. The terminal will feature 74 aerobridges, 30 remote parking bays for passenger movement to and from the aircraft and 78 boarding bridges. Six of the aerobridges will be compatible with the A380.
The terminal (roof height 27m) will also have a five level in-line baggage handling system and 160 check-in counters as well as 70 desks for immigration. Siemens Mobility has won the contract to supply the baggage handling system, which can handle more than 11,300 pieces of baggage per hour.
The building will be on two levels with departures on the upper floor and arrivals on the lower. Passengers that have checked-in will pass over a bridge to the airside and will be able to look down to the arrivals floor from the bridge.
The terminal will also have extensive shopping areas (Alpha has the duty free contract), with many restaurants and bars and executive lounges to relax in. There will also be a business centre with state-of-the-art IT and communications equipment for the business traveller.
In June 2009 DIAL announced a joint venture with the consortium comprising Ireland travel retailer Aer Rianta International and Indian Duty Free Services to develop, operate, maintain and manage duty-free outlets at Terminal 3.
The new terminal will have road access via a new six lane road from NH-8 and will also have a high-speed metro line to the city (metro line (Delhi Airport rail link) funded by DIAL and with three stations) with special city check-in facilities. There will also be an integrated 4,300-space multi-storey car park at the terminal.
The building of T3 is just the first phase of the airport development. In later stages a new cargo terminal will be built and additional passenger terminals and a new runway (eventually the secondary runway 09/27 will be realigned and extended). The passenger terminals will be added in a modular fashion to eventually form a U-shaped complex.
Passenger numbers at the airport are expected to rise to 46 million a year by 2015 and 80 million a year by 2025 (final capacity of the airport may reach 100 million passengers a year).
The airport expects to build four new terminals and a fourth runway by 2026. With the airport developing to the size of a small city DIAL will also take a leaf out of Hong Kong’s book and build an ‘aircity’ around the airport with shops, hotels, convention centres and recreational facilities for travellers.
TensorFlow for Image Classification — Top 3 Prerequisites for Deep Learning Projects
Want to train a neural network for image classification? Make sure to do this first
1 day ago · 6 min read
Recognizing objects in images is an effortless task for humans. For computers, not so much. What makes a dog a dog? And more importantly, how can computers learn these patterns? One of the sexiest C-words holds the answer. No, it’s not calculus, it’s convulutional neural network!
Today you’ll dip your toes into everything deep learni n g has to offer regarding image data. We’ll go over the basic image data preparation for deep learning — including creating a directory structure, train/test/validation split, and data visualization. Stay tuned for more deep learning articles, as I plan to cover pretty much everything related to computer vision in the upcoming weeks and months.
Don’t feel like reading? Watch my video instead:
You can download the source code on GitHub.
Introduction to image data and dataset we’ll use
Image data is significantly different from tabular data. Tabular data is made of multiple columns, each describing what you’re trying to predict. But in a way, image and tabular data are the same. Let me elaborate.
Imagine you had a 224×224 colored image. This means you have in total 50,176 pixels per channel, or 150,528 pixels in total (combining red, green, and blue channels). In theory, you could flatten the image to transform it into a tabular format — and have 150,528 columns. Doing image classification in that way is insane, to put it mildly.
You could convert the image to grayscale, which would result in 50,176 columns (pixels). It’s a good starting point, especially if you don’t need color for classification. A dog is a dog, I don’t confuse it for a microwave when displayed in grayscale.
Further, you could apply a dimensionality reduction algorithm to these 50,176 columns to keep only what’s relevant. It’s a good approach, but has a brutal flaw — you lose all 2D information.
The human ability to detect a dog in an image boils down to recognizing patterns. A row of 224 pixels means nothing, but 50 rows of 224 pixels could contain a dog’s head somewhere in the middle. It’s a combination of both height and width that makes patterns recognizable.
We’ll dive much deeper in the following articles, but this alone should make you appreciate the complexity of image data and the power of your brain to detect patterns from it.
All this talk about dogs got me thinking about the dataset we’ll use. It’s a Dogs vs. Cats dataset from Kaggle, which you can download and use for free. It’s licensed under the Creative Commons License, which means you can use it for free:
It’s a fairly large dataset — 25,000 images distributed evenly between classes (12,500 dog images and 12,500 cat images). The dataset should be large enough to train a decent image classifier from scratch.
Download it if you’re following along, and extract the PetImages folder somewhere on your machine. Here’s how it should look like:
It’s not structured optimally, so you’ll learn how to fix that in the following section.
Creating a directory structure for deep learning projects
The PetImages folder has two subfolders — Cat and Dog . The images aren’t split into training, testing, and validation sets. That’s a requirement if you want to train the model properly.
Let’s create a proper directory structure before addressing the split. We’ll have a data folder with three subfolders — train , validation , and test . Each of the subfolders will have two subfolders — dog and cat . These represent the class names, so make sure to get them right. It’s a common pattern for deep learning projects, and you should make as many folders as you have distinct classes.
Let’s start with library imports. All of these are built into Python, except matplotlib :
Next, we’ll declare a couple of variables. We’ll use the pathlib module for path management. I’ve found it much more user-friendly than the os module. Declare variables for the root data directory, and for each of the three subfolders. Finally, the last three variables represent the ratio of data for each subset:
Finally, let’s declare a function for creating the directory structure. It creates the subset directories if they don’t exist, and creates the dog and cat subdirectories inside each. The function also prints the directory tree when finished: