kandi X-RAY | arcface Summary
kandi X-RAY | arcface Summary
ArcSoft face recognition SDK for windows java. Including SDK v1.1 to v2.0
Top functions reviewed by kandi - BETA
- Load an AsvlScreen
- Convert RGB RGB color to ITU color
- Get I420 from BufferedImage
- Generate a 2 - color RGB color for BRA
- Gets all faces
- Parse the data structures
- Get the face configurations
- Get the face coordinates
- Get the pitchs values
- Uninit ASF
- Create a deep copy of this FaceFeature
- Get the roll numbers
- Get the statuses
- Dumps this feature
- Returns the field order
- Get field order
- Get gender
- Gets the field order
- Get YAWAY value
- Get face3Dangle
- Get age info
- Compare two features
- Extract face info
- Gets the genders
- Gets age
- Convert an image to a buffered image
arcface Key Features
arcface Examples and Code Snippets
Trending Discussions on arcface
Hello guys I've joined a university-level image recognition competition.
In the test, they will give two images (people face) and my model need to detect pair of the image is the same person or not
My model is resnet18 with IR block and SE block. and it will use Arcface loss.
I can use only the MS1M dataset with a total of 86876 classes
The problem is that loss is getting better, but accuracy is 0 and not changing.
Here's part of code I'm working on.
ANSWERAnswered 2022-Mar-07 at 03:30
You can try to use a smaller
m in ArcFace, even a minus value.
I am trying to implement a model with the ArcFace Layer: https://github.com/4uiiurz1/keras-arcface
to this extend I created a tf.data.dataset like so:...
ANSWERAnswered 2021-May-21 at 15:02
I think you should do it like this:
When I am importing the package ArcFace....
ANSWERAnswered 2020-Sep-11 at 08:54
This is part of project keras-arcface but it is not part of
keras so you have to install/copy it separatelly.
If you put it in your project in subfolder
arcface then it should work.
I'm trying to train Arcface with reference to.
As far as I know, Arcface requires more than 200 training epochs on CASIA-webface with a large batch size.
Within 100 epochs of training, I stopped the training for a while because I was needed to use GPU for other tasks. And the checkpoints of the model(Resnet) and margin are saved. Before it was stopped, its loss recorded a value between 0.3~1.0, and training accuracy was mount to 80~95%.
When I resume the Arcface training by loading the checkpoint files using load_sate, it seems normal when the first batch is processed. But suddenly the loss increased sharply and the accuracy became very low.
Its loss suddenly became increased. How did this happen? I had no other way so anyway continued the training, but I don't think the loss is decreasing well even though it is a trained model over 100 epochs...
When I searched for similar issues, they told the problem was that the optimizer was not saved (Because the reference github page didn't save the optimizer, so did I. Is it true?
ANSWERAnswered 2020-Oct-27 at 17:03
if you see this line!
you are Decaying the learning rate of each parameter group by gamma.
This has altered your learning rate as you had reached 100th epoch. and moreover you had not saved your optimizer state while saving your model.
This made your code to start with the starting lr i.e 0.1 after resuming your training. And this spiked your loss again.
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No vulnerabilities reported
You can use arcface like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the arcface component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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