PaliGemma 2 model card
Transformers PaliGemma 2 3B weights fine-tuned on a mixture of academic tasks using 448x448 input images.
PaliGemma 2 mix checkpoints are fine-tuned on a diverse set of tasks and are ready to use out of the box while pt checkpoints are pre-trained and intended for further fine-tuning. These tasks include short and long captioning, optical character recognition, question answering, object detection and segmentation, and more.
The model is available in the bfloat16 format for research purposes only.
Resources and technical documentation:
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).
PaliGemma 2 is based on Gemma 2, and you can find information on the
pre-training datasets for Gemma 2 in the
Gemma 2 model card.
Data responsibility filtering
The following filters are applied to WebLI, with the goal of training PaliGemma
2 on safe and responsible 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
child sexual abuse imagery (CSAI), pornography, vulgarities, or is 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.
Use in Transformers
You can use the following prompt templates to perform different tasks:
"cap {lang}": Raw short caption (from WebLI-alt)"caption {lang}": Nice, COCO-like short captions"describe {lang}": Longer, more descriptive captions"ocr": Optical character recognition"answer {lang} {question}": Question answering about the image contents"question {lang} {answer}": Question generation for a given answer"detect {object} ; {object}": Locate listed objects in an image and return the bounding boxes for those objects"segment {object}": Locate the area occupied by the object in an image to create an image segmentation for that object
1from transformers import (
2 PaliGemmaProcessor,
3 PaliGemmaForConditionalGeneration,
4)
5from transformers.image_utils import load_image
6import torch
7
8model_id = "google/paligemma2-3b-mix-448"
9
10url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
11image = load_image(url)
12
13model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto").eval()
14processor = PaliGemmaProcessor.from_pretrained(model_id)
15
16prompt = "describe en"
17model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(model.device)
18input_len = model_inputs["input_ids"].shape[-1]
19
20with torch.inference_mode():
21 generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
22 generation = generation[0][input_len:]
23 decoded = processor.decode(generation, skip_special_tokens=True)
24 print(decoded)
Here is a
notebook
that showcases fine-tuning PaliGemma 2.
Implementation information
Hardware
PaliGemma 2 was trained using the latest generation of Tensor Processing Unit
(TPU) hardware (TPUv5e).
Software
Training was completed 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. We report results on
different resolutions to provide an impression of which tasks benefit from
increased resolution and which tasks benefit from larger models. 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
Additional Benchmarks
| Model | Precision | Recall | F1 |
|---|
| PaliGemma 2 3B | 81.9 | 70.7 | 75.9 |
| Model | Precision | Recall | F1 |
|---|
| PaliGemma 2 3B | 73.8 | 74.5 | 74.2 |
| Model | S-TEDS | TEDS | GriTS-Top | GriTS-Con |
|---|
| PaliGemma 2 3B | 99.2 | 98.9 | 99.4 | 99.2 |
| Model | S-TEDS | TEDS | GriTS-Top | GriTS-Con |
|---|
| PaliGemma 2 3B | 97.6 | 97.3 | 97.9 | 97.8 |
| 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 | 7.7 | 28.4 |
| PaliGemma 2 10B | 521 | 7.5 | 20.3 |
- avg#char: Average number of characters
- avg#sent: Average number of sentences
- NES: Non entailment sentences
| Model | CIDEr | BLEU4 | Rouge-L | RadGraph F1 |
|---|
| PaliGemma 2 3B | 19.9 | 14.6 | 31.9 | 28.8 |
| PaliGemma 2 10B | 17.4 | 15.0 | 32.4 | 29.5 |
| Model | VSR zeroshot split (test) | VSR random split (test) |
|---|
| PaliGemma 2 3B | 74.8 | 81.6 |
| PaliGemma 2 10B | 79.8 | 86.8 |
Ethics and safety
Evaluation approach
Our evaluation methods include structured ethics and safety evaluations across
relevant content policies, 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 | 3B | 10B | 28B | 3B | 10B | 28B | 3B | 10B | 28B |
|---|
| 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. Prohibited uses of Gemma models are outlined in the
Gemma Prohibited Use Policy.
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-tuning to specialized tasks. Hence, its "out of
the box" or "zero-shot" performance might lag behind models designed
specifically for general purpose use.
- PaliGemma 2 is not a multi-turn chatbot. It is designed for a single round
of image and text input.
Citation
1@article{
2 title={PaliGemma 2: A Family of Versatile VLMs for Transfer},
3 author={Andreas Steiner and André Susano Pinto and Michael Tschannen and Daniel Keysers and Xiao Wang and Yonatan Bitton and Alexey Gritsenko and Matthias Minderer and Anthony Sherbondy and Shangbang Long and Siyang Qin and Reeve Ingle and Emanuele Bugliarello and Sahar Kazemzadeh and Thomas Mesnard and Ibrahim Alabdulmohsin and Lucas Beyer and Xiaohua Zhai},
4 year={2024},
5 journal={arXiv preprint arXiv:2412.03555}
6}
