Possibility of channel-by-channel image normalization when adding metadata
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- cpp, tensorflow
- Domain
- machine-learning
Research direction
The reported entry point is metadata verification in MediaPipe Studio, where per-channel image normalization currently fails. Start by tracing that verification and graph-start path; done means metadata with separate mean and std values for each channel verifies successfully and the graph starts.
Written by the indexing model from the issue text.
Description
My model is trained on a certain mean and std:
mean = [151.2405, 119.5950, 107.8395]
std = [63.0105, 56.4570, 55.0035]
The problem is that the attempt to add these values to the metadata fails at the verification stage in mediapipe studio: "Error: UNIMPLEMENTED: Per-channel image normalization is not available.; Initialize was not ok; StartGraph failed."
Did I understand correctly that there is no possibility of channel-by-channel normalization? Are there any plans to add it? Unfortunately, if I normalize all channels with one number, the quality of my model deteriorates significantly.
- Dominant language
- C++
- Stars
- 442
- Forks
- 145
- PR merge metrics
- No merged PRs in 30d
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