Support for custom optimized ONNX models
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- Dominant language
- Python
- Stars
- 4.2k
- Forks
- 385
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
Thanks for this great tool! As mentionned in this closed issue #48, @MaartenGr @sujithjoseph did you manage to deal with ONNX backend to support custom optimized ONNX models?
Contributor guide
No contributing guide indexed for this repository
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 reading closed issue #48 and the discussion in this issue to determine the expected ONNX backend behavior. Identify how custom optimized ONNX models would be provided and what compatibility or output criteria define support; no implementation files or tests are named in the payload.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100