Daniel Bourke mrdbourke

	Function	Command
	Get a list of all your environments	conda env list
	Get a list of all the packages installed in your current active environment	conda list
	Create an environment called [ENV_NAME]	conda create --name [ENV_NAME]
	Create an environment called [ENV_NAME] and install pandas and numpy	conda create --name [ENV_NAME] pandas numpy
	Activate an environment called [ENV_NAME]	conda activate [ENV_NAME]
	Create an environment folder called env in the current working directory (e.g. /Users/Daniel/project_1/) and install pandas and numpy	conda create --prefix ./env pandas numpy
	Activate an environment stored in a folder called env which is located within /Users/Daniel/project_1/	conda activate /Users/daniel/project_1/env
	Deactivate an environment	conda deactivate
	Export your current active environment to a YAML file called environment (see why below)	conda env export > environment.yaml

Function	Command
Get a list of all your environments	`conda env list`
Get a list of all the packages installed in your current active environment	`conda list`
Create an environment called [ENV_NAME]	`conda create --name [ENV_

	# Example of a Detectron2 label for image "1e2c50b991a82ee8.jpg"
	[{'annotations': [{'bbox': [228.0, 12.0, 791.0, 858.0],
	'bbox_mode': <BoxMode.XYXY_ABS: 0>,
	'category_id': 0}],
	'file_name': 'image_folder/1e2c50b991a82ee8.jpg', # this will change depending on where your images are stored
	'height': 867,
	'image_id': 33,
	'width': 1024}]

	# What downloading image labels from Open Images looks like
	import pandas as pd
	val_annots = pd.read_csv("https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv")
	val_annots.head()

	import pandas as pd
	classnames = pd.read_csv("https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv",
	names=["LabelName", "ClassName"])
	classnames.head()

	# Test our annotation formatting function
	val_annots_formatted = format_annotations(image_folder="validation", # validation path
	annotation_file="validation-annotations-bbox.csv", # validation annotations
	target_classes=subset_classes) # (fireplace & coffeemaker)

	val_annots_formatted.head()

	# Create list of validation image dictionaries
	val_img_dicts = get_image_dicts(image_folder="validation", # validation images
	annotation_file="validation-annotations-bbox.csv", # these get formatted automatically
	target_classes=target_classes) # list of target classes you're working with

	>>> Using validation-annotations-bbox.csv for annotations...
	On dataset: validation/
	Classes we're using:
	Coffeemaker 21
	Name: ClassName, dtype: int64

	# Object detection models from the Detectron2 model zoo
	models_to_try = {
	# model alias : model setup instructions
	"R50-FPN-1x": "COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml",
	"R50-FPN-3x": "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml",
	"R101-FPN-3x": "COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml",
	"X101-FPN-3x": "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml",
	"RN-R50-1x": "COCO-Detection/retinanet_R_50_FPN_1x.yaml",
	"RN-R50-3x": "COCO-Detection/retinanet_R_50_FPN_3x.yaml",
	"RN-R101-3x": "COCO-Detection/retinanet_R_101_FPN_3x.yaml"

	import numpy as np
	import pandas as pd

	np.random.seed(0)

	sales_amounts = np.random.randint(20, size=(5, 3))

	weekly_sales = pd.DataFrame(sales_amounts,
	index=["Mon", "Tues", "Wed", "Thurs", "Fri"],
	columns=["Almond butter", "Peanut butter", "Cashew butter"])

	# List of FashionMNIST labels, source: https://github.com/zalandoresearch/fashion-mnist
	class_names = ["T-shirt/top",
	"Trouser",
	"Pullover",
	"Dress",
	"Coat",
	"Sandal",
	"Shirt",
	"Sneaker",
	"Bag",