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Last Updated: June 13, 2023 Fact Checked

This article was co-authored by wikiHow staff writer, Rain Kengly . Rain Kengly is a wikiHow Technology Writer. As a storytelling enthusiast with a penchant for technology, they hope to create long-lasting connections with readers from all around the globe. Rain graduated from San Francisco State University with a BA in Cinema. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 226,138 times. Learn more...

Do you want to create a Wikipedia account? While you don't need an account to make edits to Wikipedia, this can help if you want to access protected pages, set personal settings, create a page for yourself, and more. You don't need an email address, but it's recommended in case you need to recover your account. This wikiHow will show you how to sign up for a Wikipedia account using your computer or mobile device.

Things You Should Know

• Go to Wikipedia's Account Creation page in a desktop or mobile browser.
• Enter a username and password. Make sure to add an email address to recover your account, if needed.
• Enter the CAPTCHA characters and click "Create your account".

• If you want to create an account in another language, go to https://www.wikipedia.org/ and select a language. Then, click Create account (in the chosen language) in the top-right corner. [1] X Research source

• Don't choose a username that promotes another business, flags you as a vandal, impersonates an admin, or violates any guidelines. Those will get blocked quickly.
• Keep in mind you won't be able to fully delete your account , but you can request a username change.

• You can complete the optional form on the next page, or click Skip this survey to begin using Wikipedia.
• Contribute to Wikipedia by writing articles , editing articles, and more.

Community Q&A

• An account that is auto-confirmed can upload files and edit semi-protected pages. An account is auto-confirmed after four days (plus 10 edits) from registering. Thanks Helpful 0 Not Helpful 0

• Never share your password with anyone. If you forget your password and you have an email address linked, you'll be able to reset it. Thanks Helpful 0 Not Helpful 0

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Upload is a comedy science-fiction television series that premiered on Prime Video on May 1, 2020. The series was created by Greg Daniels .

Season 2 will premiere on March 11, 2022.

• 2 Cast and characters
• 4.1 Key art
• 5 External links
• 6 References

Synopsis [ ]

Official synopsis for Upload from Amazon Prime Video:

Cast and characters [ ]

• Robbie Amell as Nathan Brown
• Andy Allo as Nora Antony
• Allegra Edwards as Ingrid Kannerman
• Zainab Johnson as Aleesha Morrison
• Kevin Bigley as Luke Crossley
• Owen Daniels as A.I. Guy

Seasons [ ]

Gallery [ ], key art [ ].

External links [ ]

• Upload on Prime Video

References [ ]

• 1 Nathan Brown
• 2 Ingrid Kannerman
• 3 Nora Antony

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Train Custom Data

📚 This guide explains how to train your own custom dataset with YOLOv5 🚀. See YOLOv5 Docs for additional details. UPDATED 13 April 2023.

Before You Start

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7 . Models and datasets download automatically from the latest YOLOv5 release .

Train On Custom Data

Creating a custom model to detect your objects is an iterative process of collecting and organizing images, labeling your objects of interest, training a model, deploying it into the wild to make predictions, and then using that deployed model to collect examples of edge cases to repeat and improve.

1. Create Dataset

YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data. There are two options for creating your dataset before you start training:

1.1 Collect Images

Your model will learn by example. Training on images similar to the ones it will see in the wild is of the utmost importance. Ideally, you will collect a wide variety of images from the same configuration (camera, angle, lighting, etc.) as you will ultimately deploy your project.

If this is not possible, you can start from a public dataset to train your initial model and then sample images from the wild during inference to improve your dataset and model iteratively.

1.2 Create Labels

Once you have collected images, you will need to annotate the objects of interest to create a ground truth for your model to learn from.

Roboflow Annotate is a simple web-based tool for managing and labeling your images with your team and exporting them in YOLOv5's annotation format .

1.3 Prepare Dataset for YOLOv5

Whether you label your images with Roboflow or not, you can use it to convert your dataset into YOLO format, create a YOLOv5 YAML configuration file, and host it for importing into your training script.

Create a free Roboflow account and upload your dataset to a Public workspace, label any unannotated images, then generate and export a version of your dataset in YOLOv5 Pytorch format.

Note: YOLOv5 does online augmentation during training, so we do not recommend applying any augmentation steps in Roboflow for training with YOLOv5. But we recommend applying the following preprocessing steps:

• Auto-Orient - to strip EXIF orientation from your images.
• Resize (Stretch) - to the square input size of your model (640x640 is the YOLOv5 default).

Generating a version will give you a point in time snapshot of your dataset so you can always go back and compare your future model training runs against it, even if you add more images or change its configuration later.

Export in YOLOv5 Pytorch format, then copy the snippet into your training script or notebook to download your dataset.

Now continue with 2. Select a Model .

1.1 Create dataset.yaml

COCO128 is an example small tutorial dataset composed of the first 128 images in COCO train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. data/coco128.yaml , shown below, is the dataset config file that defines 1) the dataset root directory path and relative paths to train / val / test image directories (or *.txt files with image paths) and 2) a class names dictionary:

After using an annotation tool to label your images, export your labels to YOLO format , with one *.txt file per image (if no objects in image, no *.txt file is required). The *.txt file specifications are:

• One row per object
• Each row is class x_center y_center width height format.
• Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.
• Class numbers are zero-indexed (start from 0).

The label file corresponding to the above image contains 2 persons (class 0 ) and a tie (class 27 ):

1.3 Organize Directories

Organize your train and val images and labels according to the example below. YOLOv5 assumes /coco128 is inside a /datasets directory next to the /yolov5 directory. YOLOv5 locates labels automatically for each image by replacing the last instance of /images/ in each image path with /labels/ . For example:

2. Select a Model

Select a pretrained model to start training from. Here we select YOLOv5s , the second-smallest and fastest model available. See our README table for a full comparison of all models.

Train a YOLOv5s model on COCO128 by specifying dataset, batch-size, image size and either pretrained --weights yolov5s.pt (recommended), or randomly initialized --weights '' --cfg yolov5s.yaml (not recommended). Pretrained weights are auto-downloaded from the latest YOLOv5 release .

💡 ProTip: Add --cache ram or --cache disk to speed up training (requires significant RAM/disk resources). 💡 ProTip: Always train from a local dataset. Mounted or network drives like Google Drive will be very slow.

4. Visualize

Comet logging and visualization 🌟 new.

Comet is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with Comet Custom Panels ! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!

Getting started is easy:

ClearML Logging and Automation 🌟 NEW

ClearML is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML:

• pip install clearml
• run clearml-init to connect to a ClearML server ( deploy your own open-source server here , or use our free hosted server here )

You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).

You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the ClearML Tutorial for details!

Local Logging

Training results are automatically logged with Tensorboard and CSV loggers to runs/train , with a new experiment directory created for each new training as runs/train/exp2 , runs/train/exp3 , etc.

This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.

Results file results.csv is updated after each epoch, and then plotted as results.png (below) after training completes. You can also plot any results.csv file manually:

Once your model is trained you can use your best checkpoint best.pt to:

• Run CLI or Python inference on new images and videos
• Validate accuracy on train, val and test splits
• Export to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
• Evolve hyperparameters to improve performance
• Improve your model by sampling real-world images and adding them to your dataset

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA / CUDNN , Python and PyTorch preinstalled):

• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training , validation , inference , export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

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