SKU: 43467855938

AXIS F2135-RE Fisheye Sensor (02641-001)

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Description

AXIS F2135-RE Fisheye Sensor (02641-001)AXIS F2135 RE Fisheye Sensor * Discreet fisheye sensor with 1080p Up to 60 fps at 1080p Up to 180 fps at 720p 185 horizontal field of view Rugged sensor unit IP66 IP67 IP6K9K rated AXIS F2135 RE is designed for use with main units and cables from Axis F Modular Camera Series with SMA FAKRA connectors to withstand vibrations and ensure a ruggedized installation. It offers up to 1080p at 60 fps, excellent 185 field of view, and good low light

AXIS F2135-RE Fisheye Sensor *

Discreet fisheye sensor with 1080p

  • Up to 60 fps at 1080p 
  • Up to 180 fps at 720p
  • 185° horizontal field of view
  • Rugged sensor unit
  • IP66/IP67/IP6K9K rated

AXIS F2135-RE is designed for use with main units and cables from Axis F Modular Camera Series with SMA-FAKRA connectors to withstand vibrations and ensure a ruggedized installation. It offers up to 1080p at 60 fps, excellent 185° field of view, and good low-light performance in color.

Multi-purpose

Ideal for installation indoors and outdoors, in machines, buses, and emergency vehicles, this fisheye sensor uses cables from Axis with SMA-FAKRA connectors to withstand vibrations and ensure a ruggedized installation. It can be installed practically anywhere- in tight spaces, behind glass, metal, or even plastic sheeting. Reliable under rough conditions, AXIS F2135-RE can withstand temperatures as low as -40 °C and as high as 70°C intermittently (-40 °F to 158 °F).

High-quality video usability

This fisheye sensor delivers high-quality video even in challenging and low light conditions. With a 185° field of view, it’s perfect when you need wide-area coverage with great details for identification purposes. It supports HDTV 1080p at 60 fps or 720p at 30 fps on 4-channels. It also supports 720p at 180 fps with AXIS F9111 Main Unit. Forensic WDR provides clarity when there’s both dark and light areas in the scene. Plus, Lightfinder captures sharp, color images in low light.

Flexible and discreet

AXIS F2135-RE offers discreet installation with only the small sensor visible externally. Based on a divided network camera concept, this fisheye sensor allows for maximum flexibility and connects to the main unit using a ruggedized SMA-FAKRA connector. Cable type and length (up to 30 m/ 98 ft) are chosen based on your specific needs. Furthermore, there’s a variety of accessories available for mounting options and for flexible installation.

Please note: The AXIS F2135-RE Fisheye Sensor will need an AXIS F91xx Main Unit.

 

5-year warranty, PVC free

 Datasheets, Manuals, Dimensions, Declarations & Statements

Product's expected availability: 3-7 business days.
Click here or call (831) 428-3438 for details.


* Images, Data Sheet & Marketing Description may differ from the actual product.

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SKU: 43467855938

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4.8 ★★★★★
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Par
Belleville, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Bozeman, 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
Chelsea, 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
Charlottesville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Lexington, 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

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