Transformers PaliGemma 3B weights, pre-trained with 224*224 input images and 128 token input/output text sequences. The models are available in float32, bfloat16 and float16 formats for fine-tuning.
PaliGemma is a versatile and lightweight vision-language model (VLM) inspired by
PaLI-3 and based on open components such as
the SigLIP vision model and the Gemma
language model. 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.
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 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, multilinguality, etc.
The following filters are applied to WebLI, with the goal of training PaliGemma
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 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.
How to Use
PaliGemma is a single-turn vision language model not meant for conversational use,
and it works best when fine-tuning to a specific use case.
You can configure which task the model will solve by conditioning it with task prefixes,
such as “detect” or “segment”. The pretrained models were trained in this fashion to imbue
them with a rich set of capabilities (question answering, captioning, segmentation, etc.).
However, they are not designed to be used directly, but to be transferred (by fine-tuning)
to specific tasks using a similar prompt structure. For interactive testing, you can use
the "mix" family of models, which have been fine-tuned on a mixture of tasks. To see model
google/paligemma-3b-mix-448 in action,
check this Space that uses the Transformers codebase.
The following snippets use model google/paligemma-3b-mix-224 for reference purposes.
The model in this repo you are now browsing may have been trained for other tasks, please
make sure you use appropriate inputs for the task at hand.
Output: Un auto azul estacionado frente a un edificio.
Running other precisions on CUDA
For convenience, the repos contain revisions of the weights already converted to bfloat16 and float16,
so you can use them to reduce the download size and avoid casting on your local computer.
This is how you'd run bfloat16 on an nvidia CUDA card.
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 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 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.
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
Maximum
Median
Maximum
Median
Maximum
Median
Toxicity
0.04%
0.03%
0.08%
0.00%
0.09%
0.00%
Identity Attack
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Insult
0.06%
0.04%
0.09%
0.07%
0.16%
0.00%
Threat
0.06%
0.05%
0.14%
0.05%
0.17%
0.00%
Profanity
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.
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. 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 model 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 was designed first and foremost to serve as a general pre-trained
model for transfer to specialized tasks. Hence, its "out of the box" or
"zero-shot" performance might lag behind models designed specifically for
that.
PaliGemma is not a multi-turn chatbot. It is designed for a single round of
image and text input.
