DF subset, we observe diagrams and charts dominating. This aligns with how these datasets are sourced. It also provides quantitative motivation for including diversified datasets in our data mixture to address limitations in larger but more general image pools.
Figure D.3: Breakdown of the main image datasets into Graphic and Photograph categories. We observe that most datasets are roughly evenly split between graphics and photographs. The main outlier is the MINT-1T (arXiv) dataset, which consists overwhelmingly of graphics.
Figure D.4: Breakdown of the main image datasets by coarse graphic type. The datasets are relatively evenly distributed across the different image types. Composite graphics seem to be overrepresented in the datasets. Furthermore, MINT-1T-ArXiv is an outlier, as most images belong to the Diagram/Chart class. This is consistent with the dataset’s sourcing, as it consists of scientific papers.
Figure D.5: Breakdown of the main image datasets by coarse photograph type. We observe that all datasets are significantly unbalanced. The Artifacts/Objects class is significantly overrepresented in all datasets. The second most represented class is People. The remaining classes are roughly equally represented, with 5–10% of the total images.
Table D.1: Standard deviation (percentage points) of renormalized category shares across categories, per dataset, for each grouping. We observe significant variation across the different domain and type classes.
Dataset
Graphic
Coarse
Photograph
Coarse
Graphic
Fine
Photograph
Fine
LLaVA-OneVision-1.5
10.38
12.87
4.29
3.15
BLIP3-Grounding-50M
9.25
19.83
4.10
4.24
Recap-DataComp-1B
9.68
18.97
3.97
4.11
MINT-1T (arXiv)
37.25
10.43
15.87
4.15
MINT-1T (PDF)
11.16
11.24
3.05
3.25
MINT-1T (HTML)
10.22
12.70
3.49
3.26
Average
14.66
14.34
5.80
3.69
Figure D.6: Breakdown of the main image datasets by fine graphic type. We observe that the Brand Logo, Book or Magazine Cover, Scientific Plot, Scientific / Technical Imagery, and Quote or Text Overlay Composite types are overrepresented in the image distribution. By contrast, the Data Table, Geographic or Spatial Map, and Flowchart or Process Diagram classes are relatively underrepresented.
Figure D.7: Breakdown of the main image datasets by fine photograph type.
Qualitative check. The second step consists of a qualitative evaluation of the proposed taxonomy. Specifically, we construct a per-label montage containing representative high-confidence examples for each of the 61 fine-grained categories together with their top-5 CLIP prediction scores. This visualization enables direct inspection of the image classes and model confidence across the taxonomy. The resulting examples demonstrate that most category images are visually coherent, supporting the validity of the proposed taxonomy for analyzing our image datasets. Figure D.8 shows samples from several prominent fine-grained classes.
Figure D.8: Image samples from several prominent fine-grained classes. We observe that our embedding and classifier pipeline correctly captures the diverse classes in our taxonomy.
Task signals. The broad task view in Figure D.9 focuses on training signals. Captioning is the largest signal across the entire training mixture. Classification and recognition, grounding and counting, visual question answering, and OCR and document understanding also occur across the inventory. The task labels overlap and their shares are not mutually exclusive.
Figure D.9: Prevalence of broad overlapping task signals.
Appendix E Tokenizer Reconstruction by Category
Table E.1gives the per-category values behind Figure 6 and the averages of Table 1.
Table E.1: Reconstruction quality and token cost of the Emu3 and Emu3.5 tokenizers on 350 Stage 1 training images, 50 per source category, grouped into six representative categories of Section 2.2.3 and preprocessed as in training. Tokens are the mean per image, arrows give the better direction, and bold marks the better tokenizer per row an