Perception LM
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Model Details
Perception Language Model (PLM) is a state-of-the-art, fully open and reproducible MLLM for transparent research in image and video understanding. It was introduced in "PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding".
Model Overview: PLM consists of a vision encoder with a small scale (<8B parameters) LLM decoder. We start by an analysis of standard training pipelines with available data, without any proprietary model distillation. We investigate large-scale synthetic data and establish key scaling laws to identify critical data gaps that limit video understanding performance, especially for spatio-temporal reasoning and fine-grained understanding tasks. To fill these gaps, we create 2.8M high-quality human-labeled. This release is nearly an order of magnitude larger than the largest existing video datasets.
We provide the training and evaluation code at perception_models codebase. You can find more details in the GitHub repo.
| Resource | Description | Documentation |
|---|---|---|
| Evaluation | Evaluation of PLM using lmms-eval | docs/evaluation.md |
| Training / Finetuning | Training and finetuning instructions for PLM | docs/training.md |
| PLM-VideoBench | Evaluation on PLM-VideoBench using lmms-eval | docs/plm_videobench.md |
| End-to-End Finetuning Example | End-to-end finetuning example on radiology images | docs/finetune_example.md |
| Generating Response | Generate responses using a trained model with generate.py |
generate.py |
PLM Image Benchmark Results
| Model | DocVQA | ChartQA | TextVQA | InfoQA | AI2D | OCRBench | COCO | Nocap | Flickr | MMMU | VQAv2 | OKVQA | VizWiz | MME | SEED | BLINK | CVBench | RealWorldQA | VSR | POPE |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PLM1B | 90.7 | 78.6 | 82.1 | 63.0 | 84.9 | 807 | 138.6 | 124.2 | 100.5 | 34.8 | 81.7 | 61.0 | 59.7 | 1603 | 76.3 | 46.8 | 73.8 | 67.1 | 68.8 | 88.4 |
| PLM3B | 93.8 | 84.3 | 84.3 | 74.6 | 90.9 | 830 | 144.9 | 126.5 | 98.0 | 41.2 | 84.3 | 66.8 | 64.0 | 1879 | 78.5 | 55.4 | 81.4 | 72.4 | 80.4 | 88.7 |
| PLM8B | 94.6 | 85.5 | 86.5 | 80.9 | 92.7 | 870 | 146.7 | 129.9 | 105.6 | 46.1 | 85.6 | 69.6 | 67.0 | 1989 | 79.3 | 56.0 | 81.3 | 75.0 | 82.8 | 89.9 |
PLM Video Benchmark Results
| Model | VATEX | DREAM 1K | How2QA | MVBench | NExTQA | PerceptionTest (test) | STAR | TVQA | VideoMME | TVBench | ActivityNetQA | EgoSchema (test) | TemporalBench | TOMATO | MotionBench (dev) | TempCompass (MCQ) | CGBench (clue) | Charades STA | VideoHallucer | Halluc. EventHallusion |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PLM1B | 92.5 | 34.3 | 86.4 | 70.1 | 80.3 | 72.7 | 83.7 | 50.3 | 49.2 | 50.4 | 62.5 | 60.4 | 18.2 | 25.5 | 52.2 | 64.6 | 43.6 | 55.2 | 49.2 | 79.5 |
| PLM3B | 96.1 | 37.4 | 89.4 | 74.7 | 83.4 | 79.3 | 84.8 | 55.3 | 54.9 | 58.9 | 66.2 | 66.9 | 23.4 | 30.9 | 60.4 | 69.3 | 47.2 | 57.7 | 55.5 | 76.5 |
| PLM8B | 99.7 | 35.9 | 90.7 | 77.1 | 84.1 | 82.7 | 84.9 | 59.3 | 58.3 | 63.5 | 67.3 | 68.8 | 28.3 | 33.2 | 61.4 | 72.7 | 46.4 | 58.6 | 57.7 | 77.3 |
Citation
If you find our code useful for your research, please consider citing:
@article{cho2025PerceptionLM,
title={PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding},
author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
@article{bolya2025PerceptionEncoder,
title={Perception Encoder: The best visual embeddings are not at the output of the network},
author={Daniel Bolya and Po-Yao Huang and Peize Sun and Jang Hyun Cho and Andrea Madotto and Chen Wei and Tengyu Ma and Jiale Zhi and Jathushan Rajasegaran and Hanoona Rasheed and Junke Wang and Marco Monteiro and Hu Xu and Shiyu Dong and Nikhila Ravi and Daniel Li and Piotr Doll{\'a}r and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
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