TIGER-Lab/Mantis-8B-Idefics2

🔥 Mantis

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Excited to announce Mantis-Idefics2, with enhanced ability in multi-image scenarios! It's fine-tuned on Mantis-Instruct from Idefics2-8b

Summary

• Mantis-Idefics2 is an LMM with interleaved text and image as inputs, trained on Mantis-Instruct under academic-level resources (i.e. 36 hours on 16xA100-40G).
• Mantis is trained to have multi-image skills including co-reference, reasoning, comparing, temporal understanding.
• Mantis reaches the state-of-the-art performance on five multi-image benchmarks (NLVR2, Q-Bench, BLINK, MVBench, Mantis-Eval), and also maintain a strong single-image performance on par with CogVLM and Emu2.

Multi-Image Performance

Models	Size	Format	NLVR2	Q-Bench	Mantis-Eval	BLINK	MVBench	Avg
GPT-4V	-	sequence	88.80	76.52	62.67	51.14	43.50	64.5
Open Source Models
Random	-	-	48.93	40.20	23.04	38.09	27.30	35.5
Kosmos2	1.6B	merge	49.00	35.10	30.41	37.50	21.62	34.7
LLaVA-v1.5	7B	merge	53.88	49.32	31.34	37.13	36.00	41.5
LLava-V1.6	7B	merge	58.88	54.80	45.62	39.55	40.90	48.0
Qwen-VL-Chat	7B	merge	58.72	45.90	39.17	31.17	42.15	43.4
Fuyu	8B	merge	51.10	49.15	27.19	36.59	30.20	38.8
BLIP-2	13B	merge	59.42	51.20	49.77	39.45	31.40	46.2
InstructBLIP	13B	merge	60.26	44.30	45.62	42.24	32.50	45.0
CogVLM	17B	merge	58.58	53.20	45.16	41.54	37.30	47.2
OpenFlamingo	9B	sequence	36.41	19.60	12.44	39.18	7.90	23.1
Otter-Image	9B	sequence	49.15	17.50	14.29	36.26	15.30	26.5
Idefics1	9B	sequence	54.63	30.60	28.11	24.69	26.42	32.9
VideoLLaVA	7B	sequence	56.48	45.70	35.94	38.92	44.30	44.3
Emu2-Chat	37B	sequence	58.16	50.05	37.79	36.20	39.72	44.4
Vila	8B	sequence	76.45	45.70	51.15	39.30	49.40	52.4
Idefics2	8B	sequence	86.87	57.00	48.85	45.18	29.68	53.5
Mantis-CLIP	8B	sequence	84.66	66.00	55.76	47.06	48.30	60.4
Mantis-SIGLIP	8B	sequence	87.43	69.90	59.45	46.35	50.15	62.7
Mantis-Flamingo	9B	sequence	52.96	46.80	32.72	38.00	40.83	42.3
Mantis-Idefics2	8B	sequence	89.71	75.20	57.14	49.05	51.38	64.5
$\Delta$ over SOTA	-	-	+2.84	+18.20	+8.30	+3.87	+1.98	+11.0

Single-Image Performance

Model	Size	TextVQA	VQA	MMB	MMMU	OKVQA	SQA	MathVista	Avg
OpenFlamingo	9B	46.3	58.0	32.4	28.7	51.4	45.7	18.6	40.2
Idefics1	9B	39.3	68.8	45.3	32.5	50.4	51.6	21.1	44.1
InstructBLIP	7B	33.6	75.2	38.3	30.6	45.2	70.6	24.4	45.4
Yi-VL	6B	44.8	72.5	68.4	39.1	51.3	71.7	29.7	53.9
Qwen-VL-Chat	7B	63.8	78.2	61.8	35.9	56.6	68.2	15.5	54.3
LLaVA-1.5	7B	58.2	76.6	64.8	35.3	53.4	70.4	25.6	54.9
Emu2-Chat	37B	66.6	84.9	63.6	36.3	64.8	65.3	30.7	58.9
CogVLM	17B	70.4	82.3	65.8	32.1	64.8	65.6	35.0	59.4
Idefics2	8B	70.4	79.1	75.7	43.0	53.5	86.5	51.4	65.7
Mantis-CLIP	8B	56.4	73.0	66.0	38.1	53.0	73.8	31.7	56.0
Mantis-SigLIP	8B	59.2	74.9	68.7	40.1	55.4	74.9	34.4	58.2
Mantis-Idefics2	8B	63.5	77.6	75.7	41.1	52.6	81.3	40.4	61.7

How to use

Run example inference:

import requests
import torch
from PIL import Image
from io import BytesIO

from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image


processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-8B-Idefics2") # do_image_splitting is False by default
model = AutoModelForVision2Seq.from_pretrained(
    "TIGER-Lab/Mantis-8B-Idefics2",
    device_map="auto"
)
generation_kwargs = {
    "max_new_tokens": 1024,
    "num_beams": 1,
    "do_sample": False
}

# Note that passing the image urls (instead of the actual pil images) to the processor is also possible
image1 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
image2 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
images = [image1, image2, image3]


query1 = "What cities image 1, image 2, and image 3 belong to respectively? Answer me in order."
query2 = "Which one do you recommend for a visit? and why?"
query3 = "Which picture has most cars in it?"

### Chat
### Round 1
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "image"},
            {"type": "image"},
            {"type": "text", "text": query1},
        ]
    }    
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}

# Generate
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query1)
print("ASSISTANT: ", response[0])

### Round 2
messages.append(
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": response[0]},
        ]
    }
)
messages.append(
    {
        "role": "user",
        "content": [
            {"type": "text", "text": query2},
        ]
    }
)
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query2)
print("ASSISTANT: ", response[0])

### Round 3
messages.append(
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": response[0]},
        ]
    }
)
messages.append(
    {
        "role": "user",
        "content": [
            {"type": "text", "text": query3},
        ]
    }
)

prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query3)
print("ASSISTANT: ", response[0])


"""
User:  What cities image 1, image 2, and image 3 belong to respectively? Answer me in order.
ASSISTANT:  Chicago, New York, San Francisco
User:  Which one do you recommend for a visit? and why?
ASSISTANT:  New York - because it's a bustling metropolis with iconic landmarks like the Statue of Liberty and the Empire State Building.
User:  Which picture has most cars in it?
ASSISTANT:  Image 3
"""

Training

See mantis/train for details

Evaluation

See mantis/benchmark for details

Please cite our paper or give a star to out Github repo if you find this model useful

Citation

@inproceedings{Jiang2024MANTISIM,
  title={MANTIS: Interleaved Multi-Image Instruction Tuning},
  author={Dongfu Jiang and Xuan He and Huaye Zeng and Cong Wei and Max W.F. Ku and Qian Liu and Wenhu Chen},
  publisher={arXiv2405.01483}
  year={2024},
}