Working Dino implementation for Classification
Overview
A small Dino implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The xlarge variant is a reproducible starting point, not a trained model release.
Training details
- Framework: PyTorch
- Precision: fp32
- Init scheme: kaiming
- Hardware: a single consumer GPU is enough
- Typical run: minutes, not hours
Default experiment recipe
The included configuration uses lamb with a polynomial schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.
Limitations
The checkpoint is untrained and unaudited. Treat the repository as an experimental starting point; anything learned from a future trained version must be documented separately.
Repository status
One Python module contains the model and a runnable example.
Generated configuration lives in config.json; the recipe in training_args.json.
The checkpoint is an initialization artifact and should not be read as a trained model.
Scores are deliberately absent.
Files
| File | Purpose |
|---|---|
train.py |
primary artifact |
README.md |
this documentation |
config.json |
architecture configuration |
training_args.json |
default experiment settings |
model.safetensors |
initialization checkpoint |
License
Distributed under the bsd-2-clause license. External datasets keep their own terms — check them separately.
- Downloads last month
- -