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timm
Опубликована
11 сен 2026

Self-distill-checkpoints-dinov3

Классификация изображенийИзображение → label

Экспериментальные чекпойнты DINOv3 и сопутствующие логиты для сравнения классификации ImageNet-1K с обычным обучением и self-distillation.

0скачиваний за 30 дней0лайковвеса обновлены21 сен 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

Репозиторий dlsmarta содержит экспериментальные чекпойнты и материалы для проверки self-distillation на DINOv3. В README описаны ветви обучения с обычной кросс-энтропией (CE) и с добавлением дистилляции от замороженного учителя (CE+SD), а также веса моделей, состояния оптимизатора и массивы логитов для ImageNet-1K. Метаданные указывают библиотеку timm, формат PyTorch и задачу классификации изображений.

Материалы подходят для сравнения результатов обучения и анализа влияния self-distillation: в описанной основной ветви CE+SD точность Top-1 на валидации достигла 82,634% на 10-й эпохе, а на 20-й составила 82,624%. Также представлены контрольные CE-ветви и эксперименты с адаптацией линейной головы TTT. Результаты выбраны по валидационной выборке и одному запуску, поэтому сами по себе не доказывают обобщаемого превосходства метода. Доступ к скачиванию требует отдельного одобрения для каждого репозитория; лицензия указана как other.

Сценарии применения

  • сравнение CE и self-distillation при дообучении DINOv3
  • анализ точности классификации ImageNet-1K по эпохам
  • воспроизведение экспериментов с дистилляцией учителя
  • сравнение контрольных чекпойнтов и оптимизаторов
  • исследование адаптации линейной головы TTT

Ограничения

В README результаты названы валидационными, выбранными по одному запуску; они не являются независимым тестовым подтверждением превосходства. Для скачивания файлов требуется отдельное ручное одобрение, а лицензия обозначена как other.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

DINOv3 experiments — results, checkpoints and optimizers

Start here: the first table row is one complete three-file CE+SD demonstration — before logits, after logits and correct labels. The following rows cover every published arm, including the overall (best) result and best CE-only control. Click an arm for its explanation, recipe and full epoch/step table.

All accuracies below are ImageNet-1K validation Top-1. Peak is not the same as final. Both companion archives, shared labels, reports and exclusions are in this table.

Experiment / armBefore: full logitsAfter: full logits and every retained epoch/stepCheckpoint + optimizer / global parentValidation Top-1: peak versus final
START HERE — one CE+SD run, exactly three fileslogits_before.npy — fitted epoch 0logits_after.npy — CE+SD epoch 10; correct_labels.npyNo model loading needed; compare the three arrays directly80.586% → 82.634% (+2.048 pp)
Original-teacher CE+SD — main runFast5 → LR 1e-4 (best)E0: shared before CE / CE+SDAll E1–10, E15–20; E11–14 logits absent; E10 peak; E20 finalAll epochs; E10 peak; E20 final82.634% @ epoch 10 (best) — overall; final 82.624% @ epoch 20
CE-only — main supervised controlFast5 → LR 1e-4E0: shared before CE / CE+SDAll E1–20; E14 peak; E20 finalAll epochs; E14 peak; E20 final82.170% @ epoch 14 (best) — CE controls; final 82.086% @ epoch 20
Shared fitted reference — before every original CE and CE+SD armSame starting point, not a separate CE-only baselineEpoch 0 logits; All reference aliasesFitted reference model (head-fit state is not a global SGD continuation)80.586%, direct-image evaluation
Original-teacher CE+SD — lower LR floorFast5 → LR 1e-6E0: shared before CE / CE+SDAll E1–15; E14 peak; E15 finalAll epochs; E14 peak; E15 final82.294% @ epoch 14; final 82.284% @ epoch 15
CE-only — lower LR floorFast5 → LR 1e-6E0: shared before CE / CE+SDAll E1–15; E14 peak; E15 finalAll epochs; E14 peak; E15 final81.622% @ epoch 14; final 81.620% @ epoch 15
Original-teacher CE+SD — higher LR floorFast5 → LR 1e-3E0: shared before CE / CE+SDAll E1–15; E6 peak; E15 finalAll epochs; E6 peak; E15 final81.676% @ epoch 6; final 80.006% @ epoch 15
CE-only — higher LR floorFast5 → LR 1e-3E0: shared before CE / CE+SDAll E1–15; E6 peak; E15 finalAll epochs; E6 peak; E15 final80.154% @ epoch 6; final 78.164% @ epoch 15
CE10 → CE, repeat the scheduleRepeat Fast5 → LR 1e-4CE epoch 10All E11–20; E20 peakAll epochs; E20 peak80.868% @ epoch 20; final 80.868% @ epoch 20
CE10 → CE+SD, refresh teacher to CE10Repeat Fast5 → LR 1e-4CE epoch 10All E11–20; E20 peakAll epochs; E20 peak81.766% @ epoch 20; final 81.766% @ epoch 20
CE+SD10 → CE+SD, refresh teacher to CE+SD10Repeat Fast5 → LR 1e-4CE+SD epoch 10All E11–20; E19 peak; E20 finalAll epochs; E19 peak; E20 final81.976% @ epoch 19; final 81.950% @ epoch 20
Historical CE — slower cosine to 1e-6E0: shared before CE / CE+SDAll E1–10; E10 peakAll epochs; E10 peak81.208% @ epoch 10; final 81.208% @ epoch 10
Historical CE — slower cosine to 1e-4E0: shared before CE / CE+SDAll E1–10; E10 peakAll epochs; E10 peak80.840% @ epoch 10; final 80.840% @ epoch 10
Historical CE — constant LR 0.005E0: shared before CE / CE+SDAll E1–10; E1 peak; E10 finalAll epochs; E1 peak; E10 final74.990% @ epoch 1; final 68.086% @ epoch 10
Head-only TTT from original reference — SGD 0.01Step 0: matched cached baselineAll Steps 0–20; Step20 peakStarting global checkpoint only; no adapted query states81.432% @ step 20 (best) — original-reference SGD grid; final 81.432% @ step 20
Head-only TTT from original reference — SGD 0.001Step 0: matched cached baselineAll Steps 0–20; Step19 peak; Step20 finalStarting global checkpoint only; no adapted query states81.228% @ step 19; final 81.228% @ step 20
Head-only TTT from original reference — SGD 0.0001Step 0: matched cached baselineAll Steps 0–20; Step20 peakStarting global checkpoint only; no adapted query states80.758% @ step 20; final 80.758% @ step 20
Head-only TTT from original reference — SGD 0.000001Step 0: matched cached baselineAll Steps 0–20; Step17 peak; Step20 finalStarting global checkpoint only; no adapted query states80.590% @ step 17; final 80.590% @ step 20
Head-only TTT from CE20 — Adam 0.01Step 0: matched cached baselineAll Steps 0–50; Step0 peak; Step50 finalStarting global checkpoint only; no adapted query states82.084% before → 81.098% final (step 50); no adapted step wins
Shared row identities and labelsApplies to every published array50,000 sample IDs; 50,000 targets; Format—Every array is 50,000 × 1,000 FP32 raw logits, not probabilities
Model, loss and hyperparameter detailsLogits archiveComplete methodsCheckpoints archive; Same methodsArchitecture, fitted head, parent/teacher identities, optimizer, masking, preprocessing and TTT
Full results, figures and explanationsPublished fixed-endpoint and paired-logit analysesRead the full report; Offline HTMLEvidence ZIPIncludes correction/harm, calibration, saliency and logit-overlap evidence
Machine-readable indexes, coverage and provenanceCoverage / missing filesTraining CSV; TTT CSV; Logit provenanceCheckpoint provenance; Retention contractHashes stay in provenance; old artifact paths remain valid
Engineering smoke/canary checkpoints — not scientific resultsNot extra experimental armsNo production-logit comparisonEngineering-only indexExcluded from best-result rankings
Later TTT studies — not published hereNormalized CE20 / CE+SD head TTT and raw-feature paper600No download in this release; Explicit exclusionsNo implied publicationDo not mistake the older SGD50 / Adam60 rows for these later runs

What the arm names mean

An arm is one experimental configuration, not an individual image. CE is ordinary supervised cross-entropy; CE+SD adds learning from a frozen teacher, with exactly 0.5 × clean CE + 0.5 × masked forward KL. An original teacher is the shared fitted epoch-0 model. A refreshed teacher is the named CE10 or CE+SD10 model, frozen for that branch.

Fast5 → floor means cosine decay from LR 0.005 to that floor over five epochs, then hold. Cosine10 instead decays for ten epochs. Constant LR never decays. The repeated Fast5 branches restart the schedule phase after epoch 10 while retaining the parent's optimizer state. Their “before” links therefore point to CE10 or CE+SD10, not epoch 0.

TTT adapts only a fresh linear-head copy for each query, using labelled training neighbours; the backbone and master head stay frozen. Its numbers are update steps, not global training epochs. Use the row's cached step-0 baseline: original-reference TTT starts at 80.588%, and CE20 TTT at 82.084%. These are not bit-identical to direct-image epoch 0 (80.586%) or CE20 (82.086%).

A harm is different from an arm: the baseline was correct and the candidate becomes wrong. A correction is baseline-wrong → candidate-correct. Neither count alone establishes a net improvement; the baseline must be named.

Reading the table correctly

  • (best) overall: the highest observed validation Top-1 is 82.634%, original-teacher CE+SD / Fast5 → 1e-4 / epoch 10. It is not the last checkpoint.
  • (best) among CE controls: 82.170%, CE / Fast5 → 1e-4 / epoch 14. The original fixed epoch-20 CE result remains 82.086%; Adam TTT still starts from CE20, not CE14.
  • The SGD TTT best label is within its own original-reference SGD grid only, not an overall win. Adam60 is a negative result.
  • “Best” is descriptive, validation-informed, single-seed selection, not untouched-test evidence or a claim of superiority on every metric. Peak and fixed/final endpoints are displayed separately.
  • All original CE/CE+SD rows share one fitted epoch-0 reference: pretrained backbone plus already-fitted ImageNet head. The legacy /ce/ reference folder also applies to CE+SD. Its checkpoint's head-fit epoch 10 is global fine-tuning epoch 0.
  • Original-teacher CE+SD at 1e-4 has every checkpoint + optimizer from E1–20, but raw logits only at E1–10 and E15–20. E11–14 scalar results do not substitute for missing arrays.
  • Click the arm name or All link for a plain-language description and one row per epoch/step. Every global-training arm page includes its correct before row; every TTT page keeps its global parent separate from unretained adapted state.

Access and file semantics

Cards are public; downloads require separate manual approval on each repository. Existing permissions, licensing, payload bytes and old links are unchanged. Each logits file contains all validation images and all classes; pair it with the shared IDs/targets from the table. The model file bundles retained global model/optimizer state; a TTT parent link is not a per-query adapted model archive.

Hashes are provenance, not the reader-facing experiment names. The new navigation follows main; actual array/checkpoint links pin the existing immutable payload revisions. Avoid materializing both legacy and readable layouts when downloading the archive.

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · timm · image-classification

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