SimCLR self-supervised pretraining

Contrastive (NT-Xent) pretraining on real handwritten digits with a large batch; a few-shot linear probe on the frozen features beats a random encoder on held-out digits.

Trained from scratch in Ropedia Academy — an interactive, bilingual course on embodied & spatial AI. Educational model: small and quick to train; the value is the method and a reproducible pipeline, not a leaderboard score. Try it live in the Ropedia demos Space.

At a glance

Dataset

• Name: Handwritten digits (UCI / scikit-learn)
• Type: real — public dataset
• Size / stats: 1,797 real 8×8 digit images, 10 classes; 256/batch contrastive (unlabeled); probe = 60 labels, averaged over 8 random subsets / 540 test
• Split: 1,257 train / 540 test; few-shot linear probe
• Source: scikit-learn load_digits (UCI Optical Recognition of Handwritten Digits)

Training config

Adam (lr 1.5e-3, cosine), 2500 steps, batch 256; NT-Xent τ=0.5; proj 64→128→64 + light cutout. Probe: 60 labels, averaged over 8 subsets (Adam lr 1e-2).

Evaluation results

Robustness (mean ± std over 5 seeds)

Single-run numbers above are one seed; this is the distribution over independent re-trains (honest variance — no cherry-picking).

Inference example

import torch, torch.nn as nn
enc = nn.Sequential(nn.Conv2d(1,32,3,1,1), nn.ReLU(), nn.MaxPool2d(2),
                    nn.Conv2d(32,64,3,1,1), nn.ReLU(), nn.AdaptiveAvgPool2d(1), nn.Flatten())
enc.load_state_dict(torch.load("encoder.pt", map_location="cpu")); enc.eval()
# images: (N,1,8,8) float in [0,1]  ->  features = enc(images)   # (N, 64)

Limitations

Educational scale. Trained quickly on CPU on small or synthetic data, so absolute numbers are not competitive with production systems — the value is the method and a reproducible pipeline. No large-scale data, no hyperparameter sweep, and no multi-seed variance is reported. Not for production use.

Features are learned on 8×8 digits — they will not transfer to natural images; needs a large batch to work.

Failure cases

Wrong augmentations (e.g. horizontal flips on digits) destroy the signal; collapses with too-weak/too-strong augmentation.

Reproduce / train your own

One click: open the notebook in Colab → Runtime → GPU → Run all, then run its Publish to the Hugging Face Hub cell.

From a shell:

git clone https://github.com/ChaoYue0307/ropedia-academy.git && cd ropedia-academy
pip install torch numpy matplotlib scikit-learn scikit-image gymnasium
jupyter nbconvert --to notebook --execute notebooks/training/C_simclr_pretrain.ipynb --output run.ipynb
# optional: override training length, e.g.  STEPS=2000  (or EPISODES=600)  before running

Files

• encoder.pt
• figure.png
• metrics.json
• seeds.png

License

Code & weights: MIT (this repository) — educational use encouraged.
Handwritten-digits data: UCI ML Repository via scikit-learn — CC BY 4.0.

Citation

If you use this model or the course materials, please cite:

@misc{ropedia_academy,
  title  = {Ropedia Academy: an interactive course on embodied & spatial AI},
  author = {Ropedia Academy},
  year   = {2026},
  howpublished = {\url{https://chaoyue0307.github.io/ropedia-academy/}}
}

Method / original work: Chen et al., A Simple Framework for Contrastive Learning (SimCLR), ICML 2020.

Related assets

• 🚀 Live demos: https://huggingface.co/spaces/cy0307/ropedia-demos
• 🤗 All trained models + collection: https://huggingface.co/cy0307
• 📚 Course & all labs: https://chaoyue0307.github.io/ropedia-academy/ · Labs tab
• 💻 Source / notebooks: github.com/ChaoYue0307/ropedia-academy

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Part of the Ropedia Academy trained-model collection. Contributions & issues welcome on GitHub.