docling-project/TableFormerV2
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test-uint8-input
#1 opened 6 days ago
by
auerchristoph
auerchristophย
published a model 12 days ago
asnassarย
updated a model 28 days ago
vkuropiatnykย
published a dataset about 1 month ago
auerchristophย
updated a model about 1 month ago
auerchristophย
published a model about 1 month ago
auerchristophย
updated a model about 1 month ago
Normalize label names
#11 opened about 1 month ago
by
auerchristoph
Add ONNX export
#10 opened about 1 month ago
by
auerchristoph
Update README.md
#7 opened 3 months ago
by
EslimD
PeterWJStaarย
published a dataset about 2 months ago
lucas-morinย
authored 6 papers 5 months ago
MolGrapher: Graph-based Visual Recognition of Chemical Structures
Paper
โข 2308.12234 โข Published
Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
Paper
โข 2501.17887 โข Published
โข 1
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
Paper
โข 2503.11576 โข Published
โข 155
Advanced Layout Analysis Models for Docling
Paper
โข 2509.11720 โข Published
MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures
Paper
โข 2503.16096 โข Published
โข 1
SubGrapher: Visual Fingerprinting of Chemical Structures
Paper
โข 2504.19695 โข Published
anditoย
authored a paper 5 months ago
Post
2409
Finally, our new paper is out! "๐๐ถ๐ป๐ฒ๐ฉ๐ถ๐๐ถ๐ผ๐ป: ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐๐ ๐๐น๐น ๐ฌ๐ผ๐ ๐ก๐ฒ๐ฒ๐ฑ"! ๐ฅณ
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
๐ finding and cleaning data at scale
๐งน removing excessive duplicates across sources
๐ค decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
๐ To celebrate the paper, Iโm also releasing a concatenated and shuffled version of the full dataset! ๐
Itโs ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
๐ finding and cleaning data at scale
๐งน removing excessive duplicates across sources
๐ค decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
๐ To celebrate the paper, Iโm also releasing a concatenated and shuffled version of the full dataset! ๐
HuggingFaceM4/FineVision_full_shuffled Itโs ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
Post
3077
Many VLMs claim to process hours of video. But can they follow the story?๐ค
Today, we introduce TimeScope: The benchmark that separates true temporal understanding from marketing hype. Let's see how much VLMs really understand!โณ
We test three skills that matter for real-world use:
๐ Localized Retrieval: Find a specific action.
๐งฉ Information Synthesis: Piece together scattered clues.
๐ Fine-Grained Perception: Analyze detailed motion (e.g., count how many times a person swings an axe).
The results are in, and they're revealing. Only Gemini 2.5 pro handles 1-hour-long videos.
Performance drops sharply with duration, proving that long video understanding is still challenging. We've found the breaking pointsโnow the community can start fixing them.๐
Want to learn more? TimeScope is 100% open-source. Benchmark your model and help us build the next generation of video AI.
๐ Blog:
https://huggingface.co/blog/timescope-video-lmm-benchmark
๐ฉโ๐ป Leaderboard & Demo: Apollo-LMMs/TimeScope
๐ Dataset: Apollo-LMMs/TimeScope
โ๏ธ Eval Code: https://github.com/EvolvingLMMs-Lab/lmms-eval
Today, we introduce TimeScope: The benchmark that separates true temporal understanding from marketing hype. Let's see how much VLMs really understand!โณ
We test three skills that matter for real-world use:
๐ Localized Retrieval: Find a specific action.
๐งฉ Information Synthesis: Piece together scattered clues.
๐ Fine-Grained Perception: Analyze detailed motion (e.g., count how many times a person swings an axe).
The results are in, and they're revealing. Only Gemini 2.5 pro handles 1-hour-long videos.
Performance drops sharply with duration, proving that long video understanding is still challenging. We've found the breaking pointsโnow the community can start fixing them.๐
Want to learn more? TimeScope is 100% open-source. Benchmark your model and help us build the next generation of video AI.
๐ Blog:
https://huggingface.co/blog/timescope-video-lmm-benchmark
๐ฉโ๐ป Leaderboard & Demo: Apollo-LMMs/TimeScope
๐ Dataset: Apollo-LMMs/TimeScope
โ๏ธ Eval Code: https://github.com/EvolvingLMMs-Lab/lmms-eval