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feat: Use new TableFormer model weights and default to accurate model version (#1100)

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* feat: New tableformer model weights [WIP]

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>

* Updated TF version

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Updated tests, after merging with Main, Switched to Accurate TF model by default

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

---------

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>
Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>
Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
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Christoph Auer
2025-03-11 10:53:49 +01:00
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parent 5e30381c0d
commit eb97357b05
43 changed files with 213 additions and 229 deletions
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tests/data/groundtruth/docling_v2/2206.01062.md
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@@ -97,21 +97,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif
Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
| | | % of Total | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) |
|----------------|---------|--------------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten |
| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a |
| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 |
| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a |
| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 |
| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 |
| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 |
| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 |
| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 |
| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 |
| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 |
| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 |
| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 |
| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) |
|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten |
| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a |
| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 |
| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a |
| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 |
| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 |
| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 |
| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 |
| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 |
| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 |
| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 |
| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 |
| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 |
Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
@@ -154,7 +154,7 @@ Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on D
| | human | MRCNN | MRCNN | FRCNN | YOLO |
|----------------|---------|---------|---------|---------|--------|
| | human | R50 | R101 | R101 | v5x6 |
| | | R50 | R101 | R101 | v5x6 |
| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 |
| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 |
| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 |
@@ -246,17 +246,17 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros
| Training on | labels | PLN | DB | DLN |
| PubLayNet (PLN) | Figure | 96 | 43 | 23 |
| PubLayNet (PLN) | Sec-header | 87 | - | 32 |
| PubLayNet (PLN) | Table | 95 | 24 | 49 |
| PubLayNet (PLN) | Text | 96 | - | 42 |
| PubLayNet (PLN) | total | 93 | 34 | 30 |
| | Table | 95 | 24 | 49 |
| | Text | 96 | - | 42 |
| | total | 93 | 34 | 30 |
| DocBank (DB) | Figure | 77 | 71 | 31 |
| DocBank (DB) | Table | 19 | 65 | 22 |
| DocBank (DB) | total | 48 | 68 | 27 |
| DocLayNet (DLN) | Figure | 67 | 51 | 72 |
| DocLayNet (DLN) | Sec-header | 53 | - | 68 |
| DocLayNet (DLN) | Table | 87 | 43 | 82 |
| DocLayNet (DLN) | Text | 77 | - | 84 |
| DocLayNet (DLN) | total | 59 | 47 | 78 |
| | Table | 87 | 43 | 82 |
| | Text | 77 | - | 84 |
| | total | 59 | 47 | 78 |
Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .