Model card
MTL-TabNet (WS).
Nam Tuan Ly / NIItable-recognition
Weakly-supervised end-to-end multi-task learning for table recognition. Ly et al. (2023), "Nam23WS" variant.
§ 01 · Benchmarks
Every benchmark MTL-TabNet (WS) has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-all-samples | 96.0% | #7 | — | source ↗ |
| 02 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-complex-samples | 94.4% | #7 | — | source ↗ |
| 03 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-simple-samples | 97.5% | #8 | — | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 02 · Strengths by area
Where MTL-TabNet (WS) actually performs.
§ 04 · Related models
Other Nam Tuan Ly / NII models scored on Codesota.
§ 05 · Sources & freshness
Where these numbers come from.
paper
3
results
0 of 3 rows marked verified.