Optional: frozen backbone vs full fine-tuning
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Assessment
- Difficulty
- 3/5
- Estimated time
- Half a day
- Newbie friendliness
- 45/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start in scripts/finetune.py, where the training setup and the existing full fine-tuning path live, and check how issue #37 changes it before you begin. Add a --freeze-backbone flag that trains only the classifier head, then run MobileNetV2 in both modes. Done means accuracy and epoch-time results for both modes are saved in results/.
Written by the indexing model from the issue text.
Description
Extra experiment for the discussion.
Files: scripts/finetune.py
Tasks
- Add
--freeze-backboneflag (train only the classifier) - Run for MobileNetV2 and compare accuracy and epoch time with full fine-tuning
Done when
Results of both modes saved in results/.
Depends on
#37
Close with a commit or PR message containing Closes #<this issue>.
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 0
- Avg merge
- 1m
- Merged PRs (30d)
- 3
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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