Customized .tflite Renet50 model for Object Classification on Web does not work #5537
Labels
os:linux-non-arm
Issues on linux distributions which run on x86-64 architecture. DOES NOT include ARM devices.
platform:javascript
MediaPipe Javascript issues
stat:awaiting googler
Waiting for Google Engineer's Response
task:image classification
Issues related to Image Classification: Identify content in images and video
task:object detection
Issues related to Object detection: Track and label objects in images and video.
type:modelmaker
Issues related to creation of custom on-device ML solutions
Have I written custom code (as opposed to using a stock example script provided in MediaPipe)
No
OS Platform and Distribution
Ubuntu 22.04
Python Version
3.10
MediaPipe Model Maker version
I didn't use Modelmaker, I used a PyTorch Resnet model converted with ai-edge-torch
Task name (e.g. Image classification, Gesture recognition etc.)
Image classification
Describe the actual behavior
The tutorial at codepen works for the tflite model Efficientnet, but not the model customized with ai-edge-torch
Describe the expected behaviour
As the HTML code works for the supported model tflite Efficientnet, it was supposed to work also with the customized tflite model, given that the customized model successfully loads at the MediaPipe Studio web interface at https://mediapipe-studio.webapps.google.com/home, but not in my HTML page.
Standalone code/steps you may have used to try to get what you need
The VSCode debugger shows an error.
Chrome code inspection does show these errors:
Here's my code:
The text was updated successfully, but these errors were encountered: