DataBloomHello,
I want to train with the TensorFlow Object Detection API and validate meanwhile. Is there a way without running two scripts at the same time?
What arguments would I need to use so that during training the model is also validated.
Code: model_main_tf2.py
submitted by /u/GT_King0895
[visit reddit] [comments]
The application that I make only needs to detect people.
The COCO dataset contains many people. I have used the fiftyone downloader to download the dataset with the tag of ‘persons’. However, this still downloads the label data for every other class in the image. It simply restricts the images to ones which contain a person.
So i’m training an SSD model from the model zoo. I’m training it from a checkpoint. How can I tell it to only detect instances of a person?
Is it simply a matter of removing everything from the label map except for:
item { name: "person", id: 50, display_name: "person" }
Bare in mind, this is after ive already converted the records to .tfrecord (done via roboflow). Is there something I should do before converting to tfrecord?
I’m completely new to all this.
submitted by /u/BuckyOFair
[visit reddit] [comments]
Hello
I imported a model with tensorflow 2.8’s C api and it outputs different predictions for the same test data set from the original keras model in Python. The model was exported in Python with
model.save(‘models/model1’)
I import it later on in C with:
TF_LoadSessionFromSavedModel
Do you know how I could visualize the model with weights in both Python and C to compare I am using exactly the same model with the same weights in both cases?
Thanks
submitted by /u/goahead97
[visit reddit] [comments]
I am trying to predict in C/C++ with a model previously trained in Keras with Python and the sentence
“`TF_SessionRun(Session, NULL, Input, InputValues, NumInputs, Output, OutputValues, NumOutputs, NULL, 0,NULL , Status);“`
outputs
No source available for “tensorflow::TF_TensorToTensor() at 0x7ffff52a9bdc”
Do you have any idea about a possible way to overcome this error?
Thanks
submitted by /u/goahead97
[visit reddit] [comments]
Bill Kish founded Ruckus Wireless two decades ago to make Wi-Fi networking easier. Now, he’s doing the same for computer vision in industrial AI. In 2015, Kish started Cogniac, a company that offers a self-service computer vision platform and development support. Like in the early days of Wi-Fi deployment, the rollout of AI is challenging, Read article >
The post Scooping up Customers: Startup’s No-Code AI Gains Traction for Industrial Inspection appeared first on The Official NVIDIA Blog.
NVIDIA Air automates your network through a digital twin to increase efficiencies along with other benefits.
Automation is the key to increasing operational efficiency and lowering OpEx, but it does not guarantee a successful data center deployment. While automation can confirm configuration integrity and prevent human errors in repetitive changes, it can’t validate intent and network requirements. Therefore, automation must be tested and validated before deployment, and the NVIDIA way of doing this is with a data center digital twin.
A data center digital twin network is a 1:1 simulation of a physical network environment, with logical instances of every switch, server, and cable. This enables it to be used for validating routing (BGP, EVPN), security policy compliance, automation, monitoring tools, and upgrade procedures.
This digital twin is hosted in the cloud, enabling teams to test their configuration at scale without the overhead of physical infrastructure. Data center digital twins offer a number of benefits:
NVIDIA Air is a free platform for creating network digital twins. These digital twins can be clones of existing topologies, prebuilt topologies, or custom designed networks that can scale to 1000s of switches and servers. Each server and switch in the digital twin can be spun up in the NVIDIA Air cloud hosted environment for IT teams to extract the value of testing to its full potential.
Every developer values reusable sample code, and NVIDIA has Production Ready Network Automation. We publish working Ansible playbooks for complete leaf/spine topologies with BGP & EVPN all set up for you. These playbooks are built for the NetDevOps approach of Continuous Integration and are the same playbooks our professional services team uses. The playbooks are constantly updated based on learnings & best practices from actual customer deployments-and we have made our Production Ready Automation assets available free of charge.
Testing is a tradeoff between risk and cost. On one hand, to fully validate network functionality and reduce the risk associated with change management, the test network needs to be similar to the production network. On the other hand, creating a physical replica of the production environment is expensive both in CapEx and OpEx.
Using a virtual replica via a data center digital twin can significantly reduce the costs associated with such testing.
IT teams can integrate the data center digital twin into their CI/CD pipeline, deploy new changes, validate the configuration using NetQ and deploy to production confidently. This level of integration helps drive down the cost of validation even further.
To shorten the time to deployment and decrease the risk of downtime, IT teams use NVIDIA Air to automate their testing process.
In addition to testing an ad hoc change, every change goes through a set of regression tests to eliminate degradation of the current functionality. Once both regression and ad hoc tests are passed, the ad hoc test is added to the regression tests suite and validated in future deployments.
Help your team learn best practices by testing changes in a risk-free environment by building your own data center digital twin. Easy to work with and free to use! Get started at NVIDIA Air.
For more information, see the following resources:
Hey,
I wanted to know if it is possible to conjugate verbs, assuming a regular pattern, using some form of NN. Essentially, I want to input a verb, in Arabic, and the output is the root form of the verb; e.g. “running” → “run”, or
“كَتَبَ” ← “يَكْتُب”.
I think I have a few challenges in this:
The second part is the one I am not sure about. I couldn’t find any information about relating text or words together under different labels. All I found was sentiment labels, and captioning images, which I don’t think necessarily solves my issue.
Any resources about something like this? Or anyone has any insight?
I am fairly new to TensorFlow, I don’t know where to necessarily look for answers, so any advice on where I should begin any research for future ideas would be greatly appreciated!
submitted by /u/Muscle_Man1993
[visit reddit] [comments]
submitted by /u/Senior1292
[visit reddit] [comments]
Hey guys, ML beginner here.
I’m looking into creating an ML model that predicts how long it will take to fully charge a battery (similar to what Android smartphones has on their lock screen when charging).
I basically want to give the model the current power (in Watts) used to charge, current battery capacity (in mAh), full battery capacity (in mAh) and get the remaining time as the output.
How would I go about that? What kind of data / how much of it would I have to get?
submitted by /u/LGariv
[visit reddit] [comments]
I don’t expect to become an expert. Not even close.
submitted by /u/margot309
[visit reddit] [comments]