🚀 PaliGemma 2 model card
PaliGemma 2 is a vision - language model that combines image and text processing capabilities. It's fine - tuned on various academic tasks and can perform multiple functions such as captioning, OCR, and question - answering.
Model page: PaliGemma
Transformers PaliGemma 2 28B weights are fine - tuned on a mixture of academic tasks using 224×224 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:
Terms of Use: Terms
Authors: Google
✨ Features
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:
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.
📦 Installation
There is no specific installation content provided in the original README. So this section is skipped.
💻 Usage Examples
Basic Usage
from transformers import (
PaliGemmaProcessor,
PaliGemmaForConditionalGeneration,
)
from transformers.image_utils import load_image
import torch
model_id = "google/paligemma2-28b-mix-224"
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
image = load_image(url)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto").eval()
processor = PaliGemmaProcessor.from_pretrained(model_id)
prompt = "describe en"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(model.device)
input_len = model_inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
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
Here is a notebook that showcases fine - tuning PaliGemma 2.
🔧 Technical Details
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.
📚 Documentation
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
Benchmark |
224 - 3B |
224 - 10B |
224 - 28B |
448 - 3B |
448 - 10B |
448 - 28B |
[AI2D][ai2d] |
74.7 |
83.1 |
83.2 |
76.0 |
84.4 |
84.6 |
[AOKVQA - DA][aokvqa - da] (val) |
64.2 |
68.9 |
70.2 |
67.9 |
70.8 |
71.2 |
[AOKVQA - MC][aokvqa - mc] (val) |
79.7 |
83.7 |
84.7 |
82.5 |
85.9 |
87.0 |
[ActivityNet - CAP][anet - cap] |
34.2 |
35.9 |
- |
- |
- |
- |
[ActivityNet - QA][anet - qa] |
51.3 |
53.2 |
- |
- |
- |
- |
[COCO - 35L][coco - 35l] (avg34) |
113.9 |
115.8 |
116.5 |
115.8 |
117.2 |
117.2 |
[COCO - 35L][coco - 35l] (en) |
138.4 |
140.8 |
142.4 |
140.4 |
142.4 |
142.3 |
[COCOcap][coco - cap] |
141.3 |
143.7 |
144.0 |
143.4 |
145.0 |
145.2 |
[ChartQA][chartqa] (aug) |
74.4 |
74.2 |
68.9 |
89.2 |
90.1 |
85.1 |
[ChartQA][chartqa] (human) |
42.0 |
48.4 |
46.8 |
54.0 |
66.4 |
61.3 |
[CountBenchQA][countbenchqa] |
81.0 |
84.0 |
86.4 |
82.0 |
85.3 |
87.4 |
[DocVQA][docvqa] (val) |
39.9 |
43.9 |
44.9 |
73.6 |
76.6 |
76.1 |
[GQA][gqa] |
66.2 |
67.2 |
67.3 |
68.1 |
68.3 |
68.3 |
[InfoVQA][info - vqa] (val) |
25.2 |
33.6 |
36.4 |
37.5 |
47.8 |
46.7 |
[MARVL][marvl] (avg5) |
83.5 |
89.5 |
90.6 |
82.7 |
89.1 |
89.7 |
[MSRVTT - CAP][msrvtt] |
68.5 |
72.1 |
- |
- |
- |
- |
[MSRVTT - QA][msrvtt] |
50.5 |
51.9 |
- |
- |
- |
- |
[MSVD - QA][msvd - qa] |
61.1 |
62.5 |
- |
- |
- |
- |
[NLVR2][nlvr2] |
91.4 |
93.9 |
94.2 |
91.6 |
93.7 |
94.1 |
[NoCaps][nocaps] |
123.1 |
126.3 |
127.1 |
123.5 |
126.9 |
127.0 |
[OCR - VQA][ocr - vqa] |
73.4 |
74.7 |
75.3 |
75.7 |
76.3 |
76.6 |
[OKVQA][okvqa] |
64.2 |
68.0 |
71.2 |
64.1 |
68.6 |
70.6 |
[RSVQA - hr][rsvqa - hr] (test) |
92.7 |
92.6 |
92.7 |
92.8 |
92.8 |
92.8 |
[RSVQA - hr][rsvqa - hr] (test2) |
90.9 |
90.8 |
90.9 |
90.7 |
90.7 |
90.8 |
[RSVQA - lr][rsvqa - lr] |
93.0 |
92.8 |
93.5 |
92.7 |
93.1 |
93.7 |
[RefCOCO][refcoco] (testA) |
75.7 |
77.2 |
76.8 |
78.6 |
79.7 |
79.3 |
[RefCOCO][refcoco] (testB) |
71.0 |
74.2 |
73.9 |
73.5 |
76.2 |
74.8 |
[RefCOCO][refcoco] (val) |
73.4 |
75.9 |
75.0 |
76.3 |
78.2 |
77.3 |
[RefCOCO+][refcoco+] (testA) |
72.7 |
74.7 |
73.6 |
76.1 |
77.7 |
76.6 |
[RefCOCO+][refcoco+] (testB) |
64.2 |
68.4 |
67.1 |
67.0 |
71.1 |
68.6 |
[RefCOCO+][refcoco+] (val) |
68.6 |
72.0 |
70.3 |
72.1 |
74.4 |
72.8 |
[RefCOCOg][refcocog] (test) |
69.0 |
71.9 |
70.7 |
72.7 |
74.8 |
73.7 |
[RefCOCOg][refcocog] (val) |
68.3 |
71.4 |
70.5 |
72.3 |
74.4 |
73.0 |
[ST - VQA][st - vqa] (val) |
61.9 |
64.3 |
65.1 |
80.5 |
82.0 |
81.8 |
[SciCap][scicap] |
165.1 |
159.5 |
156.9 |
183.3 |
177.2 |
172.7 |
[ScienceQA][scienceqa] |
96.1 |
98.2 |
98.2 |
96.2 |
98.5 |
98.6 |
[Screen2Words][screen2words] |
113.3 |
117.8 |
122.8 |
114.0 |
119.1 |
123.4 |
[TallyQA][tallyqa] (complex) |
70.3 |
73.4 |
74.2 |
73.6 |
76.7 |
76.8 |
[TallyQA][tallyqa] (simple) |
81.8 |
83.2 |
83.4 |
|
|
|
📄 License
The model is under the gemma
license.
⚠️ Important Note
To access PaliGemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged - in to Hugging Face and click below. Requests are processed immediately.
💡 Usage Tip
You can use the provided prompt templates to perform different tasks with the PaliGemma 2 model.
Property |
Details |
Model Type |
PaliGemma 2, a vision - language model |
Training Data |
Mixture of datasets including WebLI, CC3M - 35L, VQ²A - CC3M - 35L/VQG - CC3M - 35L, OpenImages, WIT |
License |
gemma |
Pipeline Tag |
image - text - to - text |