Measure performance and accuracy on customized dataset
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- Dominant language
- C
- Stars
- 479
- Forks
- 116
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
Is there any way to measure performance and accuracy on customized dataset (other than ad, vww, kws, ic) ?
I have tried setting EE_MODEL_VERSION to my own dataset and put the dataset under /ulp-mlperf/datasets/ directory, but I got the following error when running medium performance:
Need at least 5 inputs to run benchmark mode
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the medium-performance benchmark with EE_MODEL_VERSION set to the custom dataset and the dataset under /ulp-mlperf/datasets/. Trace why benchmark mode reports “Need at least 5 inputs” and determine the requirements for measuring performance and accuracy on datasets beyond ad, vww, kws, and ic. Done means custom-dataset evaluation works or its supported limitations are documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- c
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100