Comments (7)
I'm trying to use the facedetector model (facedetector_front.tflite) directly, but there is no documentation on the output of the model. A description of the output would be greatly appreciated.
I tried to inference BlazeFace model directly, and this is python demo code. It is a simple demo. There are many shortcomings to be improved.
- The post-processing spend a lot of time, I think it was caused by python code, maybe you can use Numba to optimize it.
- And I use original NMS method instead paper's method. If you want better performance, you can implement paper's method.
from mediapipe.
@yousifa We don't recommend using the facedetector_front.tflite model directly without the preprocessing and postprocessing calculators that the MediaPipe Android Face detection on GPU example demonstrates.
To understand better the details of the facedetector_front.tflite model architecture, i recommend reading the publication that the model is based on. You can also examine the code of the TfLiteTensorsToDetections calculator to better understand how to process the outputs of the facedetector_front.tflite model
Could you also provide more details on what are you trying to do with the facedetector_front.tflite model? What is the use case?
from mediapipe.
Closing this issue since there hasn't been any follow up from @yousifa
from mediapipe.
I'm trying to use the facedetector model (facedetector_front.tflite) directly, but there is no documentation on the output of the model. A description of the output would be greatly appreciated.
@yousifa I font this file has been removed, can you send me a copy, my email addr is : [email protected]. many thanks
from mediapipe.
from mediapipe.
@ogl4jo3 Thank you for sharing. I did a small change to the script, instead of decoding all boxes and then filter results by score, first filter results and then decode only relevant boxes, on my laptop it reduced post-processing time from ~50ms to ~10ms
Here is the gist
from mediapipe.
I'm trying to use the facedetector model (facedetector_front.tflite) directly, but there is no documentation on the output of the model. A description of the output would be greatly appreciated.
I tried to inference BlazeFace model directly, and this is python demo code. It is a simple demo. There are many shortcomings to be improved.
- The post-processing spend a lot of time, I think it was caused by python code, maybe you can use Numba to optimize it.
- And I use original NMS method instead paper's method. If you want better performance, you can implement paper's method.
Thank you for sharing. I run the python demo code,but get Wrong detection box. I don't know where is the problem.
from mediapipe.
Related Issues (20)
- Eyebrow Segmentation with Mediapipe in Python HOT 1
- online web demo yields better hand detection than its python equivalent HOT 4
- It is incorrect of using pose_landmark_full.tflite inference HOT 7
- Can't use MediaPipe in VisionOS development HOT 4
- Incorrect callback method argument for ObjectDetector.detect_async HOT 3
- No clear apks for quick testing HOT 1
- how to reduce the wasm and HOT 1
- Xcode: Duplicate Symbols (Conflicts with MLKit dependencies)
- BaseOptions() concatenates model_asset_path with path of venv/libsite-packages HOT 3
- I use python MediaPipe in Linux , How do i use GPU HOT 5
- Build Errro : BUILD.bazel:2613:19: Compiling src/amalgam/gen/avx512amx.c failed: (Exit 1): HOT 4
- When I use RGB data, errors always occur. Can I use RGB formatted images for inference in Java? HOT 1
- Build fails for Coral - Docker gcc too old for XNNPACK? HOT 6
- MediaPipe FaceDetector Module POC Error - Error: StartGraph failed: $Service "kGpuService", required by node HOT 2
- Error when building facemesh example on M1 mac HOT 6
- How to turn off the automatic pop-up gesture in gesture recognition HOT 2
- Missing API declaration Warning from Apple App Store Connect HOT 2
- couldn't use kaggle GPU HOT 2
- Mediapipe lagging with Tkinter HOT 2
- No attribute 'GenerateCpuTfLite' HOT 4
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from mediapipe.