Datasets:
task stringclasses 2
values | model stringclasses 4
values | revision stringclasses 4
values | aggregate_accuracy float64 0.68 0.98 | rep_accuracies stringclasses 8
values | n_reps int64 5 5 | wall_time_s float64 58.6 289 | peak_vram_gib float64 0.43 2.38 |
|---|---|---|---|---|---|---|---|
CIFAR100 | DeepGlint-AI/mlcd-vit-base-patch32-224 | 0862ef00ecb5825c1c79ab5944ceea56a5605113 | 0.77182 | [0.7678, 0.7748, 0.7696, 0.7728, 0.7741] | 5 | 81.9 | 0.434 |
CIFAR100 | DeepGlint-AI/mlcd-vit-large-patch14-336 | bd75627f99f12c1d2e0a40ed8c9d1f129b54405e | 0.89502 | [0.894, 0.895, 0.8942, 0.8948, 0.8971] | 5 | 259.9 | 2.378 |
CIFAR100 | openai/clip-vit-base-patch32 | 3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268 | 0.67794 | [0.6763, 0.6847, 0.6823, 0.6731, 0.6733] | 5 | 119.8 | 0.434 |
CIFAR100 | openai/clip-vit-large-patch14-336 | ce19dc912ca5cd21c8a653c79e251e808ccabcd1 | 0.79036 | [0.7898, 0.7977, 0.7902, 0.7855, 0.7886] | 5 | 288.8 | 2.378 |
CIFAR10 | DeepGlint-AI/mlcd-vit-base-patch32-224 | 0862ef00ecb5825c1c79ab5944ceea56a5605113 | 0.94288 | [0.942, 0.9418, 0.9411, 0.9445, 0.945] | 5 | 61.1 | 0.431 |
CIFAR10 | DeepGlint-AI/mlcd-vit-large-patch14-336 | bd75627f99f12c1d2e0a40ed8c9d1f129b54405e | 0.98284 | [0.9832, 0.9831, 0.9834, 0.9822, 0.9823] | 5 | 178.1 | 2.378 |
CIFAR10 | openai/clip-vit-base-patch32 | 3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268 | 0.901 | [0.9017, 0.9079, 0.8971, 0.9002, 0.8981] | 5 | 58.6 | 0.434 |
CIFAR10 | openai/clip-vit-large-patch14-336 | ce19dc912ca5cd21c8a653c79e251e808ccabcd1 | 0.95442 | [0.9582, 0.9595, 0.9534, 0.951, 0.95] | 5 | 185.8 | 2.378 |
MLCD vs CLIP on MTEB CIFAR-10/100: integration and evaluation
Evaluation results accompanying the MTEB integration of two MLCD image encoders (PR #5406, resolving issue #2571).
Two DeepGlint-AI MLCD encoders were integrated into MTEB, verified against the reference implementation, and evaluated on the official MTEB CIFAR-10/CIFAR-100 image-classification tasks alongside size-matched OpenAI CLIP baselines.
What was measured
Official MTEB image classification: 5 experiments x 16 train samples per class, full 10k test split, fp32, RTX 5090. Each model's embedding is the pooled CLS token after post_layernorm (the public MLCD checkpoints contain only the vision tower; weight-inspection verified).
Accuracy, 5-repetition mean:
| Task | MLCD base | CLIP base | MLCD large | CLIP large |
|---|---|---|---|---|
| CIFAR-10 | 94.29 | 90.10 | 98.28 | 95.44 |
| CIFAR-100 | 77.18 | 67.79 | 89.50 | 79.04 |
MLCD beat its size-matched CLIP baseline in all four predeclared comparisons (paired bootstrap, 10,000 draws, seed 20260907; Bonferroni-adjusted 98.75% two-sided intervals all above zero): CIFAR-10 +4.19 / +2.84 pp, CIFAR-100 +9.39 / +10.47 pp. These are checkpoint comparisons (training data and recipe also differ), on two related small-image datasets. They do not isolate the MLCD training objective and do not establish broad vision-task superiority.
Files
cell_scores.csv: one row per model/task: aggregate accuracy, all 5 per-experiment accuracies, wall time, peak VRAM.comparisons.csv: the 4 paired comparisons with 95% and Bonferroni 98.75% bootstrap intervals.timing.csv: matched-conditions throughput and VRAM (3 warmup batches, 5 counterbalanced blocks, fixed score-blind subset, batch 64).
What this is not
The authors' published linear-probe scores (86.8 base / 93.69 large on CIFAR-100) are not reproduced and could not be: their probe used a trained embedding head that is absent from the public HF checkpoints. These numbers are independent evaluations under MTEB's few-shot protocol, not reproductions.
Reproducibility
Pinned model revisions, dataset revisions, code fingerprints, per-experiment predictions, and the full evidence chain are archived in the contributor's project directory; the adapter and runner are included in the linked PR.
- Downloads last month
- 37