Wireless Type | Bluetooth |
---|
Intel NCSM2450.DK1 Movidius Neural Compute Stick
Purchase options and add-ons
Brand | Intel |
Connectivity Technology | USB |
Included Components | USB Stick |
Wireless Communication Standard | Bluetooth |
Processor Count | 1 |
About this item
- Neural Network Accelerator in USB Stick Form Factor
- Real-time on-device inference; no cloud connectivity required
- No additional heat-sink, no fan, no cables, no additional power supply
- Prototype, tune, validate and deploy deep neural networks at the edge
Frequently bought together
Get similar items fast
- Raspberry Pi 4 Computer Model B 8GB Single Board Computer Suitable for Building Mini PC/Smart Robot/Game Console/Workstation/Media Center/Etc.FREE Shipping by AmazonGet it as soon as Sunday, Mar 24
- Raspberry Pi 4 Model B 2019 Quad Core 64 Bit WiFi Bluetooth (4GB)Amazon's Choicein Single Board ComputersFREE Shipping by AmazonGet it as soon as Sunday, Mar 24
Important information
To report an issue with this product or seller, click here.
What's in the box
Looking for specific info?
Product information
Technical Details
Brand | Intel |
---|---|
Item model number | NCSM2450.DK1 |
Item Weight | 1.92 ounces |
Product Dimensions | 3 x 0.83 x 4.6 inches |
Item Dimensions LxWxH | 3 x 0.83 x 4.6 inches |
Number of Processors | 1 |
Computer Memory Type | Unknown |
Manufacturer | Intel |
ASIN | B076751BN8 |
Is Discontinued By Manufacturer | No |
Date First Available | September 28, 2017 |
Additional Information
Customer Reviews |
3.8 out of 5 stars |
---|---|
Best Sellers Rank | #91 in Desktop Barebones |
Warranty & Support
Feedback
Product Description
The Movidius Neural Compute Stick is a miniature deep learning hardware development platform that you can use to prototype, tune, and validate, your AI programs, specifically Deep Neural Networks. It features the same Movidius vision processing unit (VPU) used to bring machine intelligence to drones, surveillance cameras, and VR or AR headsets now, in a USB stick form factor.
Customer reviews
Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.
To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.
Learn more how customers reviews work on AmazonCustomers say
Customers like the ease of installation and speed of the single board computer. They mention that the SDK is easy to install and the getting started guide looked simple. They also appreciate the performance boost and object detection is very fast. That said, some disagree on simplicity.
AI-generated from the text of customer reviews
Customers find the installation process of the single board computer to be easy. They mention that the SDK is easy to install and the getting started guide looks simple.
"...The SDK is easy to install. I'm a beginner, and following pyimagesearch movidus tutorials, and its working great...." Read more
"I really wanted to have fun with this. The getting started guide looked simple. The video looked cool. Then I bought it...." Read more
"Install instruction did work. Worse it corrupted my opencv. Venedor hasn't responded through Amazon nor to contact on the website." Read more
Customers are satisfied with the speed of the single board computer. They mention that it is very fast, it has a magical performance boost, and the object detection is really fast.
"...2. Low power - it gets it's power from the USB port.3. Very fast - the examples that ship with it are well-known deep learning test cases and..." Read more
"the object detection is realy fast with this module. (i use it with a raspberry pi V3 and 5 frame/sec.) i love it!" Read more
"Magical performance boost..." Read more
Customers have mixed opinions about the simplicity of the board. Some mention that it works great on their desktop, while others say that it doesn't work at all.
"...a beginner, and following pyimagesearch movidus tutorials, and its working great. Need an extra computer for the SDK though." Read more
"The bad news first:1. The device is only usable on a a Raspberry Pi or a PC running Ubuntu 16.04 LTS...." Read more
"Worked great on my desktop connected to a VMware workstation virtual machine running Ubuntu 16.04 LTS...." Read more
"Works as promised. Used with a Raspberry Pi 3. Not for a beginner. Using this unit requires expertise well beyond a beginner's knowledge." Read more
Reviews with images
-
Top reviews
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
1. The device is only usable on a a Raspberry Pi or a PC running Ubuntu 16.04 LTS. Some people have reported success accessing it from virtual machines running Ubuntu 16.04 LTS but I have not been able to make that work repeatably.
2. The device and its SDK currently support a limited subset of the caffe and TensorFlow frameworks. The device itself is quite general, but the software is geared towards state-of-the-art computer vision algorithms.
3. The device and SDK currently only do inference, not training.
4. The models run with 16-bit floating point for speed.
The good news:
1. You can host it on a Raspberry Pi!
2. Low power - it gets it's power from the USB port.
3. Very fast - the examples that ship with it are well-known deep learning test cases and they run on the device in milliseconds!
4. It works out of the box if you have the hardware and operating system required.
5. There is a growing collection of pre-trained working open-source models (a "model zoo").
Summary: if you want to learn state-of-the-art computer vision algorithms and put them to work, this is the device you need. I'm hoping Intel will open up the SDK and on-device software and expand the generality of the tools for other applications.
The movidius github samples are working just fine.
On vmware, make sure to add usb controller and specify usb 3.0 compatibility.
Also, note the device defaults as a usb 2.0 device but switches to usb 3.0 when being used to run inferences/predictions.
Don't be surprised if you see this behavior.
Vmware will notify you of disconnects and reconnects when using the device so just allow it. When you encounter it, disable the prompting as it may interfere with the operation of the device -- causing time-out.
Note that you may need to allow some delay before running the sample codes. I get occassional error that the device is not present or stale connections. Retrying seems to solve the issue. Or reconnecting the usb to the VM if it didn't do so automatically.
I had one case when running the multistick sample code that caused the devices to be not detected even restarting the VM did not help. For that case, I rebooted my machine which help reset everything back to normal.
This was said to work on RPI 3, but I was hoping to try this on another SBC(RPI alternative) then integrate it to my custom SBC cluster for docker swarm.
Edit: Just to update, I did a second environment with Ubuntu 16.04 VM, new USB camera, etc. Similar results. nothing works. Little in the forums. Avoid unless you like a cool looking blue USB stick. Edit #2: So I thought I would be clever and bought the Neural Compute Stick 2. I figured as a recent device, it would have better support. WRONG. It does not even work on ARM yet. So for "deep learning" computing on the edge, you have to have a full Intel device dedicated to it.
The biggest issue is that its SDK is not mature. You can run the examples provided and they work, but once you try to do something of your own things get complicated quickly. You end up searching all over the web for examples due to the lack of documentation. If you are someone who enjoys hacking around this isn't a problem, but if you are someone who is used to purchasing something that is production ready then I would recommend saving your money. I don't look for this product to be around long. This is an intel product and its pretty obvious theres not a lot of effort being put into it.
Also you pretty much have to use Linux or a VM with linux on other OS's. Not that I mind but I think it should be clear to anyone else who is going to purchase this product.
Overall I wish this product was better. I was very hopeful, but after about a week of trying to get my custom tensorflow model work on it I am disappointed at the level of effort to takes to do something simple.