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Description
avatar goggle Walksnail Avatar Digital HD FPV GogglesThis FPV Drone goggle features high fidelity Full HD OLED displays and supports a new 1080p video transmission protocol in a compact design. A wide range of IPD and focus adjustment allows the user to tune the optics for any eye. The Avatar is packed with premium features such as USB C Video out, HD DVR recording, and an intuitive full color graphic user interface. Support for Betaflight Canvas Mode puts you in control of the on screen display. The
This FPV Drone goggle features high fidelity Full HD OLED displays and supports a new 1080p video transmission protocol in a compact design. A wide range of IPD and focus adjustment allows the user to tune the optics for any eye. The Avatar is packed with premium features such as USB-C Video out, HD DVR recording, and an intuitive full color graphic user interface. Support for Betaflight Canvas Mode puts you in control of the on screen display. The Avatar is the new standard for premium FPV.
Customizable Fit with Optional Faceplate Pad Accessories
Red Bird LHCP 5.8GHz Goggle Antennas
HD Video Output via USB-C Connector
Supports Betaflight Canvas Mode
Walksnail Avatar Patch Antennas
Adjustable Anti-Fog Ventilation
On/Off Power Button
Integrated HD DVR
Focus Adjustment
46° FOV
Specifications:
AVATAR HD GOGGLES
Transmitter Power: FCC: <30dBm: CE: <14dBm: SRRC: <20dBm: MIC: <25dBm
Micro Displays: Full HD 1080p OLED Dual Micro Displays
Inter-Pupillary Distance (IPD): 57 ~ 70mm (adjustable)
I/O Interface: Type-C, MicroSD Card, DC 5.5x2.1mm
Adjustable Focus Range: +2 to -6 Diopter
Communication Frequency 5.8GHz
IPD Mechanical Range: 28-70mm
Power Input: 6V-25.2V (2S-6S)
Screen Resolution: 1920x1080
IPD Visual Range: 54-74mm
Model: Avatar HD Goggles
SD Card: Supports 256Gb
HDMI Output: Supported
Field of View (FOV): 46°
Screen Size: .49 Inches
RED BIRD ANTENNA
Cross-Polarization Rejection: -15 to -30dBi
Radiation Efficiency: >97%
Dimensions: 24.8x15.3mm
Bandwidth: 5.6-6GHz
Average Gain: 1.9dBi
Connector: RP-SMA
Polarization: LHCP
Model: Red Bird
Weight: 4.2g
VSWR: <1.3
AVATAR PATCH ANTENNA
Frequency range (MHz): 5725-5875
Ambient temperature (℃): -40+60
HPBW: 75°H-plane75°E-plane
Front to back ratio (dB): >10
Input Impedance (Ω): 50
Power Capacity (W): 10
Interface: RP-SMA
Polarization: LHCP
Gain (dBi): 8
VSWR: <1.5
Includes:
1x Walksnail Avatar Digital HD FPV Goggles
2x Walksnail Avatar Patch Antennas
1x Lens Cleaning Cloth
4x Red Bird Antennas
1x XT60 Power Cable
1x Goggle Strap
1x Carry Case
Note：
1. These are for the Avatar HD video system only - they won't work with analogue FPV or another FPV system.
2. No battery included, compatible with 2-6S via XT60.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
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4.1 ★★★★★
Based on 24 reviews
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Product Reviews
★★★★★ 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.
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Reviewed in the United States on February 26, 2022
★★★★★ 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
★★★★★ 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
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
★★★★★ 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.
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Reviewed in the United States on March 1, 2022
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