Model training accuracy is 0%
After model training, my samples only point to one data set ( the bottle); however, they do not create points for the other classes. I’m not sure if my pictures are correlated to the accuracy
Project ID:
626059
Context/Use case:
I’m working on a project involving the object classification of different floating trash in outdoor water ( bag, bottle, can), and I’m simulating the water with an open box filled with water for image classification realism
Steps Taken:
- Placed objects in water took around 60 photos with phone for training in multiple angles/ perspective/object arrangments
- took 20 more for the test set and created impulse following the lamp/coffee edge impulse guide video
- Generated features and arrived at only 1 data set (bottles)
- tried going through with retraining and testing ( failed)
Expected Outcome:
I want to achieve at least 80% accuracy with my object detection of trash that can be found in different environments and arrangements in the water
Actual Outcome:
both data sets failed with no object detection
Environment:
- Platform: my mobile phone and the website ( beginner programming)
- Build Environment Details: N/A
- OS Version: Windows 10, 64bit
-
Custom Blocks / Impulse Configuration: I used 320x 320 for image, image processing block, image detection learning block ( coffee lamp video guide)
Logs/Attachments:
[Include any logs or screenshots that may help in diagnosing the issue]
