library_name: big_vision
license: gemma
pipeline_tag: image-text-to-text
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PaliGemma 2 model card
This is a preview and strictly confidential model only intended to test conversion and inference using transformers. Changes are not final, please report any issues you find.
Model page: PaliGemma
JAX/FLAX PaliGemma 2 10B weights for use with big_vision codebase,
fine-tuned with 448*448 input images on the DOCCI dataset.
The model is available in the bfloat16 format for research purposes only.
The fine-tune config is available at big_vision.
Resources and technical documentation:
Terms of Use: Terms
Authors: Google
Model information
Model summary
PaliGemma 2 is an update of the PaliGemma vision-language model (VLM) which incorporates the capabilities of the Gemma 2 models. The PaliGemma family of models is inspired by PaLI-3 and based on open components such as the SigLIP vision model and Gemma 2 language models. It takes both image and text as input and generates text as output, supporting multiple languages. It is designed for class-leading fine-tune performance on a wide range of vision-language tasks such as image and short video caption, visual question answering, text reading, object detection and object segmentation.
Model architecture
PaliGemma 2 is the composition of a Transformer decoder and a Vision Transformer image encoder. The text decoder is initialized from Gemma-2 in the 2B, 9B, and 27B parameter sizes. The image encoder is initialized from SigLIP-So400m/14. Similar to the original PaliGemma model, PaliGemma 2 is trained following the PaLI-3 recipes.
Inputs and outputs
- Input: Image and text string, such as a prompt to caption the image, or a question.
- Output: Generated text in response to the input, such as a caption of the image, an answer to a question, a list of object bounding box coordinates, or segmentation codewords.
Model data
Pre-train datasets
PaliGemma 2 is pre-trained on the following mixture of datasets:
- WebLI: WebLI (Web Language Image) is a web-scale multilingual image-text dataset built from the public web. A wide range of WebLI splits are used to acquire versatile model capabilities, such as visual semantic understanding, object localization, visually-situated text understanding, and multilinguality.
- CC3M-35L: Curated English image-alt_text pairs from webpages (Sharma et al., 2018). We used the Google Cloud Translation API to translate into 34 additional languages.
- VQ²A-CC3M-35L/VQG-CC3M-35L: A subset of VQ2A-CC3M (Changpinyo et al., 2022a), translated into the same additional 34 languages as CC3M-35L, using the Google Cloud Translation API.
- OpenImages: Detection and object-aware questions and answers (Piergiovanni et al. 2022) generated by handcrafted rules on the [OpenImages dataset].
- WIT: Images and texts collected from Wikipedia (Srinivasan et al., 2021). [OpenImages dataset]: https://storage.googleapis.com/openimages/web/factsfigures_v7.html
Data responsibility filtering
The following filters are applied to WebLI, with the goal of training PaliGemma 2 on clean data:
- Pornographic image filtering: This filter removes images deemed to be of pornographic nature.
- Text safety filtering: We identify and filter out images that are paired with unsafe text. Unsafe text is any text deemed to contain or be about CSAI, pornography, vulgarities, or otherwise offensive.
- Text toxicity filtering: We further use the Perspective API to identify and filter out images that are paired with text deemed insulting, obscene, hateful or otherwise toxic.
- Text personal information filtering: We filtered certain personal information and other sensitive data using the Cloud Data Loss Prevention (DLP) API to protect the privacy of individuals. Identifiers such as social security numbers and [other sensitive information types] were removed.
- Additional methods: Filtering based on content quality and safety in line with our policies and practices. [other sensitive information types]: https://cloud.google.com/sensitive-data-protection/docs/high-sensitivity-infotypes-reference?_gl=1*jg604m*_ga*ODk5MzA3ODQyLjE3MTAzMzQ3NTk.*_ga_WH2QY8WWF5*MTcxMDUxNTkxMS4yLjEuMTcxMDUxNjA2NC4wLjAuMA..&_ga=2.172110058.-899307842.1710334759
Implementation information
Hardware
PaliGemma 2 was trained using the latest generation of Tensor Processing Unit (TPU) hardware (TPUv5e).
Software
Training was done using JAX,
Flax,
TFDS and
big_vision.
JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models.
TFDS is used to access datasets and Flax is used for model architecture. The
PaliGemma 2 fine-tune code and inference code are released in the big_vision
GitHub repository.
Evaluation information
Benchmark results
In order to verify the transferability of PaliGemma 2 to a wide variety of academic tasks, we fine-tune the pretrained models on each task. Additionally we train the mix model with a mixture of the transfer tasks. We report results on different resolutions to provide an impression of which tasks benefit from increased resolution. Importantly, none of these tasks or datasets are part of the pretraining data mixture, and their images are explicitly removed from the web-scale pre-training data.
PaliGemma 2 results by model resolution and size
| Benchmark | 224-3B | 224-10B | 224-28B | 448-3B | 448-10B | 448-28B |
|---|---|---|---|---|---|---|
| AI2D | 74.4 | 82.8 | 83.1 | 75.6 | 84.6 | 84.5 |
| AOKVQA-DA (val) | 64.2 | 69.0 | 69.9 | 68.2 | 70.9 | 71.2 |
| AOKVQA-MC (val) | 78.9 | 83.1 | 84.9 | 82.4 | 86.1 | 87.1 |
| ActivityNet-CAP | 34.5 | 35.8 | - | - | - | - |
| ActivityNet-QA | 51.3 | 53.6 | - | - | - | - |
| COCO-35L (avg34) | 113.8 | 115.8 | 116.5 | 115.8 | 117.3 | 117.2 |
| COCO-35L (en) | 138.2 | 140.7 | 142.9 | 141.0 | 142.0 | 141.7 |
| COCOcap | 141.4 | 143.7 | 144.0 | 143.1 | 144.8 | 145.4 |
| ChartQA (avg) | 57.7 | 60.8 | 58.0 | 71.6 | 78.4 | 73.0 |
| CountBenchQA | 81.0 | 85.7 | 86.3 | 80.6 | 87.1 | 87.3 |
| DocVQA (val) | 39.6 | 43.0 | 45.0 | 74.1 | 76.8 | 75.7 |
| GQA | 66.4 | 67.4 | 67.3 | 68.2 | 68.6 | 68.2 |
| InfoVQA (val) | 25.5 | 33.8 | 36.3 | 37.8 | 47.8 | 46.8 |
| MARVL (avg5) | 83.4 | 89.4 | 90.8 | 82.5 | 89.1 | 89.8 |
| MSRVTT-CAP | 67.5 | 71.4 | - | - | - | - |
| MSRVTT-QA | 50.4 | 51.8 | - | - | - | - |
| MSVD-QA | 61.0 | 62.3 | - | - | - | - |
| NLVR2 | 91.5 | 93.7 | 94.1 | 91.4 | 94.0 | 94.0 |
| NoCaps | 123.4 | 126.5 | 126.9 | 123.6 | 126.9 | 127.1 |
| OCR-VQA | 73.4 | 74.6 | 75.1 | 75.5 | 76.1 | 76.4 |
| OKVQA | 64.0 | 68.0 | 71.3 | 64.5 | 68.2 | 70.8 |
| RSVQA-hr (test) | 92.7 | 92.6 | 92.7 | 92.8 | 92.8 | 92.9 |
| RSVQA-hr (test2) | 90.8 | 90.9 | 90.7 | 90.7 | 90.8 | 90.9 |
| RSVQA-lr | 93.3 | 93.7 | 93.6 | 92.3 | 93.2 | 93.3 |
| RefCOCO (testA) | 75.9 | 77.1 | 76.8 | 78.7 | 79.7 | 79.2 |
| RefCOCO (testB) | 70.8 | 74.1 | 74.0 | 73.6 | 76.1 | 74.9 |
| RefCOCO (val) | 73.6 | 75.8 | 75.0 | 76.2 | 78.3 | 77.2 |
| RefCOCO+ (testA) | 72.9 | 74.9 | 73.5 | 76.4 | 77.6 | 76.8 |
| RefCOCO+ (testB) | 64.3 | 68.1 | 67.2 | 66.7 | 71.3 | 68.6 |
| RefCOCO+ (val) | 68.6 | 71.9 | 70.1 | 71.9 | 74.5 | 72.8 |
| RefCOCOg (test) | 68.8 | 72.1 | 70.7 | 72.6 | 74.8 | 73.9 |
| RefCOCOg (val) | 68.0 | 71.3 | 70.7 | 72.3 | 74.3 | 73.1 |
| ST-VQA (val) | 62.0 | 64.2 | 65.6 | 80.5 | 81.6 | 81.9 |
| SciCap | 165.7 | 158.8 | - | 183.8 | 177.0 | - |
| ScienceQA | 95.7 | 98.4 | 98.1 | 96.4 | 98.2 | 98.7 |
| Screen2Words | 114.1 | 118.1 | 122.9 | 113.6 | 119.4 | 123.0 |
| TallyQA (complex) | 70.0 | 73.5 | 74.1 | 73.8 | 77.1 | 77.1 |
| TallyQA (simple) | 81.8 | 83.2 | 83.4 | 85.4 | 86.4 | 85.8 |
| TextCaps | 127.3 | 138.2 | 140.0 | 152.2 | 157.3 | 153.2 |
| TextVQA (val) | 59.6 | 64.0 | 64.7 | 75.2 | 76.7 | 76.2 |
| VATEX | 80.3 | 82.9 | - | - | - | - |
| VQAv2 (minival) | 82.7 | 84.0 | 84.5 | 84.6 | 85.8 | 85.8 |
| VizWizVQA (val) | 76.2 | 77.6 | 78.6 | 77.3 | 79.0 | 78.5 |
| XM3600 (avg35) | 42.6 | 44.5 | 45.0 | 43.3 | 44.5 | 45.2 |
| XM3600 (en) | 80.4 | 81.1 | 81.1 | 79.6 | 81.5 | 80.8 |
| xGQA (avg7) | 58.8 | 61.5 | 61.0 | 60.3 | 62.8 | 61.9 |
Note: Values in the table above may differ slightly from the PaliGemma 2 technical report, because these results were obtained for a single random seed.
Additional Benchmarks
| Model | Precision | Recall | F1 |
|---|---|---|---|
| PaliGemma 2 3B | 81.88 | 70.73 | 75.9 |
| Model | Precision | Recall | F1 |
|---|---|---|---|
| PaliGemma 2 3B | 73.8. | 74.54 | 74.17 |
| Model | S-TEDS | TEDS | GriTS-Top | GriTS-Con |
|---|---|---|---|---|
| PaliGemma 2 3B | 99.18 | 98.94 | 99.43 | 99.21 |
| Model | S-TEDS | TEDS | GriTS-Top | GriTS-Con |
|---|---|---|---|---|
| PaliGemma 2 3B | 97.6 | 97.31 | 97.99 | 97.84 |
| Model | CER | LER | SER |
|---|---|---|---|
| PaliGemma 2 3B | 1.6 | 6.7 | 2.3 |
- PaliGemma 2 3B, Full Match: 94.8
| Model | avg#char | avg#sent | NES % |
|---|---|---|---|
| PaliGemma 2 3B | 529 | 7.74 | 28.42 |
| PaliGemma 2 10B | 521 | 7.45 | 20.27 |
- avg#char: Average number of characters
- avg#sent: Average number of characters
- NES: Number of entailment sentences
| Model | CIDEr | BLEU4 | Rouge-L | RadGraph F1 |
|---|---|---|---|---|
| PaliGemma 2 3B | 19.9% | 14.6% | 31.92% | 28.8% |
| PaliGemma 2 10B | 17.4% | 15% | 32.41% | 29.5% |
| Model | VSR zeroshot split (test) | VSR random split (test) |
|---|---|---|
| PaliGemma 2 3B | 0.75 | 0.82 |
| PaliGemma 2 10B | 0.80 | 0.87 |
Ethics and safety
Evaluation approach
Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including:
- Human evaluation on prompts covering child safety, content safety and representational harms. See the Gemma model card for more details on evaluation approach, but with image captioning and visual question answering setups.
- Image-to-Text benchmark evaluation: Benchmark against relevant academic datasets such as FairFace Dataset (Karkkainen et al., 2021).
Evaluation results
- The human evaluation results of ethics and safety evaluations are within acceptable thresholds for meeting internal policies for categories such as child safety, content safety and representational harms.
- On top of robust internal evaluations, we also use the Perspective API
(threshold of 0.8) to measure toxicity, profanity, and other potential
issues in the generated captions for images sourced from the FairFace
dataset. We report the maximum and median values observed across subgroups
for each of the perceived gender, ethnicity, and age attributes.
Metric Perceived gender Ethnicity Age group Model size 2B 9B 27B 2B 9B 27B 2B 9B 27B Maximum Toxicity 0.14% 0.15% 0.19% 0.29% 0.39% 0.39% 0.26% 0.18% 0.32% Identity Attack 0.04% 0.02% 0.02% 0.13% 0.06% 0.06% 0.06% 0.03% 0.06% Insult 0.17% 0.25% 0.17% 0.37% 0.52% 0.52% 0.27% 0.39% 0.24% Threat 0.55% 0.43% 0.57% 0.83% 0.48% 0.48% 0.64% 0.43% 0.64% Profanity 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Median Toxicity 0.13% 0.10% 0.18% 0.07% 0.07% 0.14% 0.12% 0.08% 0.12% Identity Attack 0.02% 0.01% 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Insult 0.15% 0.23% 0.14% 0.14% 0.17% 0.13% 0.09% 0.18% 0.16% Threat 0.35% 0.27% 0.41% 0.28% 0.19% 0.42% 0.27% 0.31% 0.40% Profanity 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Usage and limitations
Intended usage
Open Vision Language Models (VLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
Fine-tune on specific vision-language task:
The pre-trained models can be fine-tuned on a wide range of vision-language tasks such as: image captioning, short video caption, visual question answering, text reading, object detection and object segmentation.
The pre-trained models can be fine-tuned for specific domains such as remote sensing question answering, visual questions from people who are blind, science question answering, describe UI element functionalities.
The pre-trained models can be fine-tuned for tasks with non-textual outputs such as bounding boxes or segmentation masks. Vision-language research:
The pre-trained models and fine-tuned models can serve as a foundation for researchers to experiment with VLM techniques, develop algorithms, and contribute to the advancement of the field.
Ethical considerations and risks
The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world image-text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading, or harmful.
- Guidelines are provided for responsible use with the model, see the Responsible Generative AI Toolkit.
- Transparency and Accountability
- This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem.
Risks identified and mitigations:
- Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases.
- Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases.
- Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided: see the Responsible Generative AI Toolkit. Prohibited uses of Gemma models are outlined in the Gemma Prohibited Use Policy.
- Privacy violations: Models were trained on data filtered to remove certain personal information and sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.
Limitations
- Most limitations inherited from the underlying Gemma 2 models still apply:
- VLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging.
- Natural language is inherently complex. VLMs might struggle to grasp subtle nuances, sarcasm, or figurative language.
- VLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements.
- VLMs rely on statistical patterns in language and images. They might lack the ability to apply common sense reasoning in certain situations.
- PaliGemma 2 was designed first and foremost to serve as a general pre-trained model for fine-tune to specialized tasks. Hence, its "out of the box" or "zero-shot" performance might lag behind models designed specifically for that.
- PaliGemma 2 is not a multi-turn chatbot. It is designed for a single round of image and text input.