Deepstack hardware requirements. We recommend Higher than Intel Core i7.

Deepstack hardware requirements 5) so I have just manually installed it and all seems fine in conjunction with Blue iris (I use YOLOv8) A few observations: BlueIris does not now report Codeproject version any more Starting DeepStack. Click the New folder in Blue Iris. root@deepstack-server:~# docker container logs blissful_gould Introduction to DeepStack. Overall, I'd say CPAI is a bit more efficient and uses less processor, but the memory requirements are also higher. Supported platforms are: Any 6th generation or newer Intel CPU will require new RAM in addition to new CPU and motherboard. Carefully evaluate your specific AI project requirements to determine the optimal hardware configuration. It can spike the CPU usage for a moment, but I haven't had any major issues with this. Step 1: best advice is to try it on your existing hardware and see what's giving out first using system monitoring. -v localstorage:/datastore This specifies the local volume where DeepStack will store all data. just need to setup LPR and i can forget about it. PC2 #. Open comment sort options. The first time you run deepstack, you need to activate it following the process below. To begin, you need to install DeepStack on a server or a compatible device. While integrating Deepstack with Google Coral TPU, keep in mind the following: In this subreddit: we roll our eyes and snicker at minimum system requirements. However, Deep Stack also runs on Nvidia Jetson boards, “New” Folder. Which cameras you have can make a big difference too. These images are passed from the API to the configured detector(s) until a match is found that meets the Explore how Frigate enhances Deepstack Coral for efficient object detection and video analysis in real-time applications. Our recommendations will be based on generalities from typical workflows. | Restackio The USB version of the Coral is particularly versatile, as it works with a wide range of hardware and does not require additional drivers. 01. Small deployments. This configuration allows Frigate to communicate with Deepstack for object detection tasks. Click Display to show your video card(s). Below we shall run DeepStack with only the FACE features enabled. Supported platforms are: NVIDIA Jetson via Docker. The following requirements describe the suggested system for the VisionPro Deep Learning application training and development PC. The type and number of objects (of any To integrate Deepstack with Frigate effectively, you will utilize the Deepstack API for object detection. If you want to use your system as a virtualization host, review the necessary hardware requirements for virtualization. TLDR:I was running BI on my desktop until some hardware troubles. Download DeepStack for free. Hardware Specifications: The performance of DeepStack can vary based on the hardware used. When selecting a CPU, a higher CPU clock speed rate and multiple core processors will result in higher runtime tool execution. jpg and latest. technically seen it might be possible assuming it doesn't only work on cuda or such. For example, older devices that leverage legacy (INTx) PCI Interrupts are not supported. I ran into some strange problems with DS, but it turned out to be a hardware issue (CPU overheating causing performance issues). AI, yes CodeProject was way slower for me but I don't know why, object type recognition was also way better with CodeProject. I run the windows version in conjunction with Blueiris, and an nvidia GPU for acceleration. PC1 #. See StarlingX Hardware Requirements to review the StarlingX Kubernetes hardware requirements. deepstack. The type and number of objects (of any On a side note, before DeepStack was integrated I built a Node-RED flow to send images to AWS Rekognition for object detection, etc. cpp:79] Could not initialize NNPACK! Reason: Unsupported hardware. Xlights then controls an led strip in each window connected to an esp8266 running wled. The time of the last detection of any target object is in the last target detection attribute. Hardware requirements for up to 5000 devices; Hardware requirements for up to 25,000 devices; Large deployments FAQs. I like Codeproject. In part 1 of this 3-part series, I cover how to install and configure BlueIris NVR and Deepstack for AI object detection. at the same time rendering times can be much longer on low end hardware, this also means way less people actually port it to Frigate + HomeAssistant is the best solution i've found. When I hit the begin stacks arrow, it begins stacking and then the program closes abruptly at 33% complete. just make a text file of the exact same name of the custom model, and blue iris will properly send the queries to deepstack. DeepStack is an AI server that empowers every developer in the world to easily build state-of-the-art AI systems both on premise and in the cloud. I used the requirements on Deepstack's website, https://docs. cc https://register. For GPU-based inference, 16 GB of RAM is generally sufficient for most use cases, allowing the entire model to be held in memory without resorting to disk swapping. Intel® Core™ Ultra Series 1 and Series 2 (Windows only) Intel® Xeon® 6 processor (preview) Intel Atom® Processor X Series. I just dont see enough availability to be commonly useful. Inference Times: Typical inference times range from 20-40 ms, depending on the YOLO model and the specific Jetson platform used. Yes I think so. This integration allows Frigate to leverage Deepstack's capabilities, enhancing its performance in detecting and tracking objects. DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker Matej Moravˇc´ık♠,♥,† , Martin Schmid♠,♥,† , Neil Burch♠ , Viliam Lis´y♠,♣ , Dustin Morrill♠ , Nolan Bard♠ , Trevor Davis♠ , Kevin Waugh♠ , Michael Johanson♠ , Michael Bowling♠,∗ ♠ Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G2E8, Canada ♥ Facial recognition, my primary use case: seems more accurate on CP. Intel Core i5 processor; 8 GB RAM; 10 GB Disk Space; Linux or Windows 10 DeepStack is device and language agnostic. For optimal results, use devices with sufficient processing power. When reading through the Microsoft docs for DDA there is a text regarding Device Requirements saying: Not every PCIe device can be used with Discrete Device Assignment. Lack of SHA Extensions results in a very significant slow down. This is basically going to continually record and store footage until your 300gb is I'm trying to use GPULab environment to build a custom model for DeepStack. Problem: Objects are not being detected accurately. Whether you are an expert or a novice, this Quick Start tutorial will take you through the basic hardware specifications of the ZimaBoard, the basic applications of the pre-built home If you are running Home Assistant on Raspberry Pi then you can run Deepstack on your Linux or Windows 10 computer. That's interesting, I wonder without hardware acceleration how does it improve accuracy without losing speed. However, it lacks some advanced features like automatic throttling Learn how to set up object detection using Agent DVR and DeepStack AI in Home Assistant. Consider the following: GPU Utilization: Ensure that the system is equipped with a compatible GPU to accelerate inference times. The hardware on which DeepStack runs can greatly influence its performance. Every task uses memory. Hardware requirements. Is it the GPU or the motherboard that is the problem here? The deepstack_face component adds an image_processing entity where the state of the entity is the total number of faces that are found in the camera image. Below are the steps to set up and configure Deepstack within your Frigate environment. There's something wonky with the way BlueIris initially starts deepstack, another user I'm looking to add cameras with h264/h265 encoding and I'm interested in the possibility of using deepstack. Compute hosts must have a minimum of 1 TB of disk space available. Intel Core i5 processor; 8 GB RAM; 10 GB Disk Space Place the custom_components folder in your configuration directory (or add its contents to an existing custom_components folder). The deepstack_object component adds an image_processing entity where the state of the entity is the total count of target objects that are above a confidence threshold which has a default value of 80%. DeepStack’s source code is Install DeepStack GPU¶ To install and use DeepStack GPU version on your Windows machine, follow the steps below. The downloads are now available for 2. This is mainly why people are having issues with these Same truck with use main stream checked (in this example the truck is still confirmed and has processing times similar to above but Deepstack setting has been ignored). I did a temporary fix by copying deepstack. DeepStack runs completely offline and independent of the cloud. 0, and Secure Boot. The class and number objects of each class is listed in the entity attributes. This setup minimizes CPU usage by leveraging your hardware capabilities. During the training process for facial recognition, CP. You can have a single target To run with the face apis, simply use -e VISION-FACE=True instead, for scene, use -e VISION-SCENE=True. For the CPU installation, just install DS, turn it on in BI, and enable it for each camera you want to use it on. HARDWARE AND SOFTWARE REQUIREMENTS. Deepstack face recognition counts faces (detection) and Frigate's hardware recommendations are here: https://docs. •Intel Core i5 My understanding is that the typical use case for multiple Deepstack instances is either a number of cameras and amount of activity that Deepstack becomes the bottleneck, or something like doing a fast analysis at a low threshold and then passing positive hits to a second instance with a higher quality analysis and threshold. Current video RAM usage is about 1 GB. An NPU is also required for advanced AI features. Strengths. All other cameras have use main stream unchecked:-Camera bit rate settings etc:-Deepstack settings (when unchecked not shown but no other change):- DeepStack AI Configuration. You can run it on Windows, Mac OS, Linux, Raspberry PI and use it with any programming language. AI offers flexibility and adaptability for Disk space requirements depend on the total number of instances running on each host and the amount of disk space allocated to each instance. i5-6500T) are underpowered versions designed to fit in smaller cases and meet stricter energy-usage requirements. For more information on setting up WSL 2 with Docker Desktop, see WSL. Add to your Home-Assistant config: DeepStack is an AI server that empowers every developer in the world to easily build state-of-the-art AI systems both on premise and in the cloud. In this article, we'll explore the key components contributing to GPU memory usage during LLM inference and how you can accurately estimate your GPU memory requirements. Found a cool game that runs fantastic on a lower end system? Great! DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU. Once installed, run the example scene recognition code to verify Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company The deepstack_object component adds an image_processing entity where the state of the entity is the total number of target objects that are found in the camera image. •Intel Core i5 It sounds like you're already aware of the hardware requirements which is good. Automate your smart home based on visual cues from IP cameras! In this section, we will Outline the hardware and setup requirements for implementing object detection in Home Assistant. We’ll also discuss advanced techniques to Your hardware choices should be prioritized as follows: GPU (graphics processing unit) RAM (random access memory) CPU (central processing unit) Hard disk storage; 1. I would suggest an i3 10th generation CPU, 2x16GB DDR4 RAM and B560 motherboard. •Intel Core i5 The choice between them should be based on specific use cases, hardware availability, and performance requirements. -p 80:5000 This makes DeepStack accessible via port 80 of the machine. Before you install the GPU Version, you need to follow the steps below. Deepstack Plex with Transcoding (Im new to BlueIris and will be using this for a new CCTV setup at home) Hardware CPU: i5-12400 (12th Gen Intel) Graphics: iGPU only RAM: 16GB Would it be best to run BI+Deepstack+Plex all on one windows DeepStack supports high speed inference on different hardware types, including CPUs and Nvidia GPUs. I have not experimented with other encodings but that’s a good idea. The importance of system memory (RAM) in running Llama 2 and Llama 3. Top. 3 GHz and the system information file shows a Core i7-7600U 2. . Rasperry Pi & ARM64 Devices via Docker. To achieve optimal performance with Deepstack, consider the following hardware options: Nvidia Jetson. I am using the latest release. I moved it off to a secondary UnRaid server and even then I wasn’t using the right HDs for that type of work. cc. The component can optionally save snapshots of the processed images. cc/windows/ <- Run all APIs command towards bottom). My Deepstack implementation randomly broke one day and BI stopped recording any alerts even though everything was still recording and all the right services were running, seemed to be in the right places, and configured properly but nothing including reinstalling would get it working again so I deleted DS and then installed CodeProject. Had blueiris running for 10 months without issues deepstack for the detection. Make sure these meet or exceed the requirements list above. Our research is supported by the International Federation of Poker , IBM , the Alberta Machine Intelligence Institute , the Natural Sciences and Engineering Research Council of Canada and the Charles University What hardware are you using? Sorry that I don't have any feedback of your question, but I recently upgraded my Pi3 to a Pi4 / 8GB, and consider installing Tensorflow / Doods / deepstack Don't know if the Pi4 can handle that, or if I should go on for a used I5. often the main problem with such things is that it is only designed for high end hardware and often needs custom tweaking to even get it to support lower end hardware. A low end Nvidia GPU supported by deepstack is probably what you want. place a copy in the custom models folder blue iris uses, or b. It's not about the hardware in your rig, but the software in your heart! Join us in celebrating and promoting tech, knowledge, and the best gaming, study, and work platform there exists. The plan is to have 10 cameras (Reolink RLC-510A [qty 5], Reolink RLC-811A [qty 4], HIKVISION DS-2CD2087G2-LU [qty 1]) Hardware Requirements upvote Dear community, I am planning on building a new HA setup including several 4k cameras together with object/face detection (deepstack and frigate). but rather seeks to achieve a cycle-accurate replication of the original computer and gaming hardware, ensuring the long-term preservation of these classic systems. Hardware Acceleration Configuration. Best. DeepStack is an AI API engine that serves pre-built models and custom models on multiple edge devices locally or on your private cloud. Deepstack scene recognition classifies an image into one of 1. For the PreCommit1 task a CPU model with support for Intel SHA Extensions: AMD since Zen microarchitecture, or Intel since Ice Lake, is a must. Deepstack shouldn't do any recognition until after person detection from the Coral. Two 4k cameras on 4 virtual cores, with Intel+VPP and Deepstack enabled. This guide will walk you through the essential steps to get DeepStack up and running efficiently. DeepStack is an open-source AI API server that empowers developers, IoT experts to easily deploy AI systems both on premise and in the cloud. Reload to refresh your session. Powershell output. Members Online. DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU HARDWARE AND SOFTWARE REQUIREMENTS DeepStack runs on any platform with Docker installed. This repo provides functionality to train object detection models on your own objects, the model Is it possible to setup a "cluster" with deepstack on the 4 servers and setup a kind of central reverse proxy that manages those 4 servers so deepstack just has the single IP option where images are sent to, the hub does the managing but the 4 servers do the hard calculation work instead of my possible NAS CPU which blue iris goes on ( i know, no hardware decoding, but The importance of hardware selection in face recognition systems is underscored by comparative studies such as those by Salih et al. Edge Devices: Deploying DeepStack on edge devices can minimize latency by processing data closer to the source. Intel Core i5 The “best” hardware will follow some standard patterns, but your specific application may have unique optimal requirements. I guess deepstack + Blue Iris isn't terrible but I haven't used it in a while. Q: Can Deep Stack run on the Raspberry Pi? A: Currently, Deep Stack does not support the Raspberry Pi. DeepStack is device Review the guidelines provided for system, hardware, security, memory, and RAID before installing. AI crashed a few times and took my whole docker stack with it. Consider the following: Use a wired connection instead of Wi-Fi for better stability. Below are the Deepseek hardware requirements for 4 If you're using custom models, they have to be installed on the machine with deepstack, and for blue iris to know they exist, either A. 2 support Mainly just the hardware requirements/extra load on my main server. I am not sure though 😛 ). Now this is a ratio, so it should be a 1. Specialized Hardware: The inclusion of Tensor Cores specifically designed for AI tasks allows RTX GPUs to accelerate complex mathematical operations that are essential for AI model training and inference. Q: What are the hardware requirements for running Deep Stack? A: For optimal performance, it is recommended to have an i5 processor and 8GB of RAM. This setup allows users to utilize Deepstack's advanced detection features alongside Frigate's robust video surveillance functionalities. On my hardware, sending the image to AWS Rekognition and getting a result is usually faster than getting a result from DeepStack. Once installed, run the example detection code to verify your installation is working. cc/windows/ You just have to love PCs. Basic Parameters-e VISION-DETECTION=True This enables the detection API. This page also has a lot of suggestions for improving Motion detection alerts can be sent by an http request/webhook. Hardware Compatibility: Ensure that your hardware is compatible with both Frigate and Deepstack to achieve optimal performance. When a person is detected in the front, Blueiris fires an mqtt command to NodeRed on Hassio. Hello community, I want to add local facial recognition to a camera in HA without adding hardware or causing performance issues to my existing config. DeepStack. To optimize performance, especially when dealing with video streams, configuring hardware acceleration is crucial. 1. g. DeepStack: Deeply Stacking Visual Tokens visual tokens, they face constraints related to convergence costs and data requirements. 8GHz, which meets (and exceeds) the minimum system requirements. (2020). After setting up Deepstack (DS) with CPU version a couple months ago I've started to notice that I quite often Make sure to replace <your_deepstack_server_ip> and <port> with the appropriate values for your Deepstack server. I used to be a Blue Iris user, then moved to Synology, and now run frigate in a docker container on my Synology. exe. We recommend using a Debian-based virtual machine, such as Proxmox DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU HARDWARE AND SOFTWARE REQUIREMENTS DeepStack runs on any platform with Docker installed. I also have Nvidia Jetson Nano 4GB RAM which (my understanding) can run Deepstack and use GPU to do object recognition. Is this is a realistic requirement? How would you implement a solution? I run HA on a Home Assistant Yellow with a 8GB CM4 Lite and 500GB NVME SSD and have two cameras available to use, a Ring internal With DeepStack apparently now deprecated in Blue Iris, leading to some users of DeepStack being reluctant to update to newer versions of BI, what then is the received wisdom on IPCT about what AI software might be reasonable to replace DeepStack with in Blue Iris? It seems like there is a beauty contest going on, will it be SenseAI or something else which will Hardware Type. Minimum Hardware Requirements for AI Applications. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. Affordable: In general, more DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU HARDWARE AND SOFTWARE REQUIREMENTS DeepStack runs on any platform with Docker installed. New. After installing deepstack I used this command to start deepstack from powershell deepstack --VISION-DETECTION True --PORT 80. To run the just the lotus-miner process without any sealing tasks, it is recommended to have at least a CPU with 8 cores. Storage: 256GB of SSD or more. 04 with the Official Docker Image (but I feel like the repo version works a bit better. 8 (I was on 2. Best Use Cases. HARDWARE AND SOFTWARE REQUIREMENTS. 1 cannot be overstated. Had a cheap Lenovo barely used with Ubuntu installed on it so I thought I'd give Frigate a go. The promises of Artificial Intelligence are huge but becoming a machine learning engineer is hard. This is a community for anyone struggling to find something to play for that older system, or sharing or seeking tips for how to run that shiny new game on yesterday's hardware. Once you initiate the run command above, visit localhost:80/admin in your browser. CPU #. Example: Fortnite requires a Core i3-3225 3. Optimize the hardware running Deepstack to ensure it meets the performance requirements. The World's Leading Cross Platform AI Engine for Edge Devices. I’m usually ~45% CPU with a bump up to 100% CPU when a deepstack event is triggered. Deepstack is a service which runs in a docker container and exposes deep-learning models via a REST API. I cover camera settings, De Note that DeepStack has no fixed requirement on the density of its lookahead tree besides those imposed by hardware limitations and speed constraints. Part 2 will cover adding cameras t Hardware Requirements¶ StarlingX OpenStack has been tested to work with specific hardware configurations. Systems with a "T" suffix on the CPU (e. AI are open-source and can be deployed on various hardware setups. However, for best performance, the following minimum requirements are highly DeepStack GPU Version serves requests 5 - 20 times faster than the CPU version if you have an NVIDIA GPU. Reply reply I still use deepstack as well. Our sensing products help deliver the real-world data you need to your machine learning system. I wanted to ask for your recommendations and experiences in terms of hardware and combination of hardware for a complete setup. DeepStack runs on the docker platform and can be used from any programming language. As mentioned above, there are no correct settings in this regard, meaning that you must understand how this all works and apply it to your requirements. The Recall AI feature is exclusive to Copilot+ PCs, which Microsoft defines as a new computer category with these hardware requirements: Processor: ARM or x86 CPU with NPU (40 TOPS). I’m planning on trying to pass GPU through to VM to use the GPU DeepStack Documentation!¶ Official Documentation and Guide for DeepStack AI Server. Can the MiSTer play N64? When the frigate/events topic is updated the API begins to process the snapshot. Click the + sign next to Components to expand the list. The performance of an Deepseek model depends heavily on the hardware it's running on. deepstack-desktop: Mon Oct 11 15:49:36 2021total used free shared buff/cache availableMem: 1979 1837 37 0 104 48Swap: 5085 1294 3791 I started to notice the system unresponsiveness here, it would take forever to do anything to include logging in, swap was getting used like crazy. Recognized faces are listed in the entity matched faces attribute. Limitations. While using Deepstack with Frigate, keep in mind the following: Network Latency: Since the integration operates over the network, expect some delay in detection times compared to local processing. (Image credit: Tom's Hardware) The write process can take some time, depending on the USB drive being used, but when done the drive can be removed and used to install Windows 11 on an older Hardware Optimization. Motion detection Face recognition via: dlib DeepStack. Hardware requirements: BlackBerry UEM. 3. DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games. Additionally, it contains a description of the performance test scenario, which you may use to check if your hardware fits the requirements. Deepstack object detection accuracy though does leave alot to be desired. You might even run Deepstack in a separate VM or container from Blue Iris so that a surge in Deepstack's resource usage won't starve the Blue Iris process of its own CPU time. Those direct plays almost all media. DeepStack runs on any platform with Docker installed. 00 if it matches the FPS. Note that by default the component will not automatically scan images, but requires you to call the image_processing. Here’s what you’d need for different model sizes if quantized to 4-bits: 7 billion parameters – 5GB RAM; 13 billion parameters – 8GB RAM First Look at the Home Server. This integration is particularly beneficial for those utilizing platforms like Raspberry Pi or Nvidia Jetson, as both DeepStack and CodeProject. Setup Deepstack I can open deepstack and load light frames. Found a cool game that runs fantastic on a lower end system? Great! Place the custom_components folder in your configuration directory (or add its contents to an existing custom_components folder). In the table below, only the Interface sections are The lowest cpu footprint for Frigate and Deepstack is to use a Coral as well as a dedicated GPU. video/hardware. To do this, run the test and compare the results with the baseline data provided. Miscalculating memory requirements can cost you significantly more in hardware or cause downtime due to insufficient resources. I'm using a Quadro P400, relatively cheap and much lower power requirements than a gaming GPU. CompreFace Image Classification Responsive, mobile friendly Web UI written in TypeScript React MQTT support Home Assistant MQTT Discovery Lookback, buffers DeepStack is an AI API engine that serves pre-built models and custom models on multiple edge devices locally or on your private cloud. If the minimum hardware requirements are not met, system performance cannot be guaranteed. - DEEPSTACK_IP : the IP address of your deepstack instance, default "localhost" - DEEPSTACK_PORT : the PORT of your deepstack instance, default 80 - DEEPSTACK_API_KEY : the API key of your deepstack instance, if you have set one - DEEPSTACK_TIMEOUT : the timeout to wait for deepstack, default 30 seconds - The integration of Deepstack with Frigate enhances the object detection capabilities by leveraging the power of AI. Note Also what motion detection you'll be using, if you want to use deepstack - if you're encoding mp4 files or saving to raw files, overlays etc etc basic advice would be at least 16gb of memory, ssd drive for recording and as fast a cpu as you can afford - GPU does help a bit but it depends on what encoding settings your cameras are using - you Hardware requirements. Red Hat Enterprise Linux supports the following architectures: I have three 1080p RTSP streams from PoE cameras. for There are a lot of guides out there on how to improve your system - hardware acceleration can help (I think blue iris 5 is required to support h265 hardware decode), dual stream recording is a huge improvement, more ram can help (I The deepstack_object component adds an image_processing entity where the state of the entity is the total count of target objects that are above a confidence threshold which has a default value of 80%. Set a limit, uncheck limit clip age, and set to delete. Then configure face recognition. 6. With everything set up correctly, six camera streams of 1080p might see about 5-8% CPU usage. If you plan to use Blue Iris's AI integrations or a large number of cameras, get at least 16 GB of memory. Supported Models: All Jetson boards, including Jetson Nano and Jetson Orin AGX, are supported. We recommend Higher than Intel Core i7. Doorbell i went with Dahua 2MP Before we check some Mac models, let’s establish some memory requirements. The rest depends on how many cameras, what resolution and what fps you need to decode. Sort by: Best. Obviously a higher learning curve though. Incorrect Object Detection. frigate. GPU (graphics processing unit) For The researchers would like to thank the professional players who committed valuable time to play DeepStack as well as our many reviewers and our families & friends. We desperately need to come together to create a hardware replacement for these devices Home Assistant custom component for Deepstack scene recognition. Hands down the best setup for motion detection without false positives. Consider starting with a base setup and scaling up as needed. You can have a single target object class, or multiple. Ensure your machine has an NVIDIA GPU. If I enable Blue Iris to use hardware video decode, video RAM usage increases. Took a few hours to integrate, but I'm really enjoying it. Deep stack typically takes ~5s to complete. By default, Frigate operates with a Update: Bought the Quadro P400, see update post below TL; DR Would I be better buying a Quadro P400 for an old Optiplex 9010 or a Jetson Nano 4GB to run DeepStack on?. RAM and Memory Bandwidth. •Intel Core i5 You signed in with another tab or window. They seem to be really efficient devices and it they could be baked into Blueiris that would really reduce the hardware requirements. Speed of processing is about the same. The interface below will appear. This is a great way to try out the system and if you like it enough, perhaps you can decide to upgrade to The following hardware prerequisites are required to successfully run WSL 2 on Windows 10 or Windows 11: 64-bit processor with Second Level Address Translation (SLAT) 4GB system RAM; Enable hardware virtualization in BIOS. AI than deepstack in the day with optimal light, but worse than deepstack in low light. AI. Blue Iris works best when the FPS and the iframes match. Performance Considerations. For more information, see Virtualization. Configuring Detectors. Ensure that your system meets the necessary requirements. Their work reveals a significant performance differential between the NVIDIA Jetson Nano and Raspberry Pi in real-time recognition tasks, indicating the crucial role of appropriate hardware in optimizing system In this subreddit: we roll our eyes and snicker at minimum system requirements. I actually made a 3-part video series all about how to install both, how to configure them, add cameras, as well as how to get camera feeds and motion detection notifications and images into HA via mqtt, and how to send notifications to the Something to consider - if you will be using AI/DeepStack, it is more efficient to run on Linux/Docker vs Windows, which saves CPU resources. Now look at the key - that is the iframes ratio. You switched accounts on another tab or window. Also, Windows 10 is up to date with In this video, I cover how to add BlueIris to Home Assistant, how to add camera feeds and alert images to the dashboard, and how to configure motion alerts a Deepstack CPU object detection speed has been pretty good. Factory new hardware will probably be limited to 10th gen CPUs or newer. scan service e. I have also configured blueiris to enable deepstack Blueiris Settings. Notes: 8 GB of RAM is recommended as a minimum for most, especially on Windows 11. As Deepstack is open source now there is no monetary Basic Parameters--gpus all This enables gpu access to the DeepStack container-e VISION-SCENE=True This enables the scene recognition API. Follow these steps: Download DeepStack: Visit the official DeepStack website to download the latest version. I am thinking about pi, server, nuc, coral stick, nvidia jetson, intel AI-stick, I Basic Parameters –gpus all This enables gpu access to the DeepStack container-e VISION-SCENE=True This enables the scene recognition API. However, for best performance, the following minimum requirements are highly recommended. Memory: 16GB or more. As mentioned also, I made a huge performance step by running deepstack on a docker on my proxmox host instead of running it in a windows vm. System requirements¶ This section provides basic information about the murano environment system requirements. Then you won't need to assign as many resources to the Blue Iris VM. sudo docker run-e VISION-FACE = True-v localstorage: / datastore-p 80: 5000 deepquestai / deepstack. is there a recommended cheap hardware like a Barebone PC with Nvidia graphics for running Agent DVR? many recordings are triggered because a) I have a light in the carport which goes on and causes the motion detection to trigger and b) when the cameras switch from IR (night mode) to day mode, motion is detected and a recording is triggered. Blueiris utilizes Deepstack AI to identify a person. We are constantly expanding the hardware support and would soon be announcing support for Windows 11 24H2 hardware requirements include a 1GHz 2-core CPU, 4GB of RAM, 64GB of SSD, TPM 2. I When using DeepStack with Frigate, consider the following: Network Latency: Since the integration operates over the network, ensure that your network is optimized to minimize latency. Virtualizinf the BI Windows VM and Docker container on the same host is ideal to get the most out of resources. Once installed, run the example detection Comparing similar alerts AI analysis between DeepStack and CodeProject. You signed out in another tab or window. It fails with the following error: [W NNPACK. Now that DeepStack is installed and configured on Blue Iris, we can move on to setting up triggers and alerts. Follow the steps below to DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU HARDWARE AND SOFTWARE DeepStack is an AI API engine that serves pre-built models and custom models on multiple edge devices locally or on your private cloud. If you would like to use this option, you need to I used DeepStack first and found it underwhelming, I used 50 high resolution photos and the score given to me was always ~80 and never higher. when I click on "test in browser" button I get the message "Deepstack is Activated" in a web browser but it doesn't seem to be DeepStack is a cross platform AI engine for performing Object Detection, Face Detection and Face Recognition on the edge and the cloud. Download and Install Recommeded Hardware Spec: While DeepStack can run successfully on a 1GB Ram device, we recommend using a device with 8GB Ram for good performance. Share Add a Comment. Basic Parameters –runtime nvidia This enables gpu access to the DeepStack container-e VISION-DETECTION=True This enables the detection API. The lookahead trees vary in the actions available to the player acting, the actions available for the opponent’s response, and the actions available to either player for the remainder of the round. A Online communities and forums dedicated to AI can provide valuable insights and hardware recommendations based on specific use cases. CPU. I also have HA running on another piece of hardware (it would be ideal if I could run it on Nano, but based on what I read, it is too much hassle). You can obtain a free activation key from register. Hardware requirements for up to 500 devices; Hardware requirements for up to 1000 devices; Hardware requirements for up to 2000 devices; Medium deployments. The Personal Computer. using an automation. Basic Parameters-e VISION-FACE=True This enables the face recognition APIs, all apis are disabled by default. To effectively run AI applications, certain hardware specifications are recommended: The deepstack_object component adds an image_processing entity where the state of the entity is the total count of target objects that are above a confidence threshold which has a default value of 80%. My BI rig is an old Optiplex 9010 (i5 3550, 8GB) which is a small form factor (SFF) case. My experience so far is that Frigate is stupid fast to get up and running on, an absolute breath of fresh air from Blue Iris, but missing some core NVR functionality that BI has even if the UI and config feels old school and Windows Recall requirements for Copilot+ PC. Intel Atom® processor with Intel® SSE4. AI and dont want to end with it. Security: TPM (Trusted Platform Module) 2. BI on one machine and Deepstack on a completely separate machine. Install DeepStack: Follow the installation instructions provided in the documentation. 1 only has one file in folder "C:\DeepStack\server" named deepstack. I then use the deepstack custom-component in HA (and deepstack container) to check if the captured image contains people, and then alert me :-) Apache Airflow is a complex, dynamic system with hardware requirements that vary based on the deployment environment, DAG complexity, and the number of tasks and DAG runs. VLAN offloading, which increases performance by adding and removing VLAN tags in hardware, rather than in the server’s main CPU; DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU HARDWARE AND SOFTWARE REQUIREMENTS DeepStack runs on any platform with Docker installed. I uninstalled Deepstack CPU and deleted the leftover folder. Change the path to the path of your “New” Windows folder. If your application will be relying on the Deepstack's forum doesn't seem to have anything. Here are some points: I use Jellyfin Media Player (desktop), Jellyfin (android), Gelli/Finamp (music in android), Kodi / Jellyfin TV App (android tv box). The iframes not matching (that you cannot fix or change with a reolink) is why they miss motion in Blue Iris and why people have problems. For users prioritizing speed and efficiency, Deepstack may be the preferred option, while CodeProject. I'm currently with nVidia 1030 GT (2 GB RAM). In parallel, LLaVA and its follow-ups [13, 76, 47, 50, 49] achieved success in connecting vision and language using a simple projection module. The next part tripped me up, but you have to shut off BlueIris and initialize Deepstack on your own through Powershell (https://docs. Sequential Processing: Good for any sort of work that either cannot be done in parallel well or has a very high single-thread performance requirement. NodeRed sends a command to an xlights sequence that has the thunder and lightning soundtrack and lighting effects. Please note that this is focused on ML/DL workstation hardware for programming model “training” rather than “inference”. For the PreCommit2 task a Hello! I am currently running it on Ubuntu Serve 20. Runs great on my DS3018xs, and with very low overhead. exe in the same folder with the file name server. It works ok with Deepstack and Codeproject, although I disabled all modules except custom object detection. Scores for unknown people was ~70 so really not much room to work with. The Deepstack page also says you can start deepstack with command like I have below: deepstack --VISION-SCENE=True --VISION-DETECTION=True --VISION-FACE=True --PORT 80 However, I have Blue Iris start DeepStack and it has "Default Object Detect" checked (which I'm thinking would be VISION-DETECTION=True if starting from a shell) How do I know if Blue I came across this Github repo that integrates DeepStack AI (for person/object recognition)with Surveillance Station. CPUs (Central Processing Units) Versatility: CPUs are general-purpose and can handle a wide variety of tasks beyond AI. DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read gpuinstall. For recommendations on the best computer hardware configurations to handle Deepseek models smoothly, check out this guide: Best Computer for Running LLaMA and LLama-2 Models. Have three cameras right now, might expand that to 5-6. However, for larger models, 32 GB or more of RAM can provide a . The hardware requirements for the Copilot+ PC include a Snapdragon X Elite CPU with NPU, 16GB of RAM, and 256GB of SSD. Deepstack face recognition counts faces (detection) and To run with the face apis, simply use -e VISION-FACE=True instead, for scene, use -e VISION-SCENE=True. A separate machine could mean a completely separate hardware server or a VM or a I didn't have much of a choice. Once installed, run the example scene recognition code to verify DeepStack version 2022. However, support for it is expected in the future. What is DeepStack ? DeepStack is an open-source AI API server that empowers developers, IoT experts, research teams and individuals in small and large companies around the world to easily deploy In this video, I cover how to add an IP camera to Blue Iris, and how to configure Deepstack AI object detection for that camera. 0. ¶ DeepStack is an AI server that empowers every developer in the world to easily build state-of-the-art AI systems both on premise and in the cloud. jpg images from Frigate's API. It greatly simplifies the difficulties of alignment tasks and even Thanks for your response and reassurance. Traceback Solution: Since Deepstack operates over the network, latency can affect performance. To ensure a smooth operation, it is crucial to continuously monitor and adjust the system's resources. deepstack you definitely want a compatible GPU - it's super taxing on CPU. Remember: These are guidelines. I would start by assigning 6 cores to the Blue Iris VM and adjust from there as needed.