SKU: 94371640710
subaru roof tent

subaru roof tent Discovery Roof Top Tent (Size M) – Mike's Custom Toys

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Description

subaru roof tent Discovery Roof Top Tent (Size M) – Mike's Custom ToysFREE SHIPPING The Discovery roof top tent opens via a clam shell mechanism (hinged on one end). Please note this model does not include an integrated storage tray (see Extreme model). Take full advantage of your roof top elevation and enjoy the 270 degree view looking out of the Discovery's large wrap around windows. To maximize airflow there are entrances on both sides of the tent, and for extreme conditions, ventilation can also be assisted by the

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The Discovery roof top tent opens via a clam shell mechanism (hinged on one end). Please note this model does not include an integrated storage tray (see Extreme model). Take full advantage of your roof top elevation and enjoy the 270-degree view looking out of the Discovery's large wrap-around windows.

To maximize airflow there are entrances on both sides of the tent, and for extreme conditions, ventilation can also be assisted by the solar-powered ventilation system. Access to the tent is easily gained by using the folding ladder that comes standard with all James Baroud roof top tents and attaches to either side of the tent.

Exterior Dimensions

Interior Dimensions
Length: 79"

Length: 78"
Width: 55"

Width: 54.25"
Height (closed): 13"

Height: 41"
Height (open): 44"

 

Tent Fabric

We guarantee you won't find a higher-quality, better-performing fabric on any roof top tent. All James Baroud tents utilize a proprietary aluminized polyester with acrylic coating that is 100% waterproof, breathable, UV-resistant, and solar-reflective. All windows are covered with quality insect-proof netting.

Mattress

One of the most important features of a roof top tent is the part you sleep on: the mattress. All James Baroud roof top tents feature a thick, moisture-resistant 3" high-density foam mattress. Mattresses have a heavy duty zippered cover for easy removal and cleaning. 

Interior Storage

All tents include a removable storage pocket for stowing keys, wallets, headlamps, clothing or anything else you need to quickly stash inside the tent. Hard shell tents include an additional large ceiling storage net.  

Assisted Opening

Patented gas strut-assisted opening makes set up incredibly fast and easy - less than 30 seconds, and nearly as quick to collapse back down. 

Simple Mounting

Hard shell tents are simple to install. Roof top tents feature reinforced mounting rails integrated into the lower shell/platform of the tent, and include installation brackets allowing easy direct mounting to most aftermarket roof rack systems.

Ladder

Entry to the tent is easily gained by using the specially-designed telescoping aluminum ladder. This unique design automatically adjusts to the height of your vehicle and prevents bending while you're in the tent.

Durability & Extreme Weather Performance

James Baroud employs a former Dakkar Rally driver to conduct testing on each tent. All James Baroud tents have passed a grueling durability test, and you can sleep in confidence knowing that James Baroud tents are waterproof and tested in winds up to 60 mph. For added comfort in extreme temperatures, add a Comfort Mat under your mattress (see Accessories). 

Outer Shell

The outer shell of all James Baroud hard shell tents (available in 3 colors) utilizes fiberglass-fortified polyester. Aerodynamic ribs on the upper shell help decrease wind noise and increase fuel economy as well as provide additional structural strength and rigidity. 

LED Lighting

All hard shell tents feature a bright 15-LED rechargeable flashlight. Made of tough polycarbonate with an integrated belt clip, the light may be removed from the tent and carried around camp to illuminate your way wherever you go. Includes a charge indicator LED as well as a USB charging cord. 

Ventilation

James Baroud hardshell tents feature a unique electric ventilation fan powered by a sealed solar panel on top of the tent shell. The ventilation fan is completely waterproof and can run on 24 hours on a single charge. Additional air vents with integrated dust filters on each side of the tent provide air circulation inside while the tent is closed. 

Industry Leading Warranty

James Baroud roof top tents come with the most inclusive warranty in the market: 5 years for hardshell tents. Warranty covers manufacturer's defects and failure of the tent fabric, shell, and strut mechanism. James Baroud tents are designed to allow replacement of any part of the tent by your local authorized dealer- so just get in touch with us here at Rhino Adventure Gear should you encounter any issues with your roof top tent.

Shipping Notes
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Exchange/Return Notes
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SKU: 94371640710

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4.1 ★★★★★
Based on 29 reviews
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Verified Purchase
Richard Hackathorn
Los Angeles, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Cuba, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Lexington, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Lowell, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
New York, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 1, 2022

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