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Model: OpenGVLab/Mini-InternVL2-4B-DA-BDD Source: Original Platform
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README.md
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README.md
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---
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license: mit
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pipeline_tag: image-text-to-text
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library_name: transformers
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base_model:
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- OpenGVLab/InternVL2-4B
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base_model_relation: merge
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language:
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- multilingual
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tags:
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- internvl
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- custom_code
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---
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# Mini-InternVL
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[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)
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[\[🗨️ InternVL Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#data-preparation)
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## Introduction
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We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing.
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These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains.
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<table>
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<tr>
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<th>Model Name</th>
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<th>HF Link</th>
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<th>Note</th>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-Drivelm</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Drivelm">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Drivelm">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Drivelm">🤗4B</a></td>
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<td> Adaptation for <a href="https://github.com/OpenDriveLab/DriveLM/tree/main/challenge"> CVPR 2024 Autonomous Driving Challenge </a></td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-BDD</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-BDD">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-BDD">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-BDD">🤗4B</a></td>
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<td> Fine-tuning with data constructed by <a href="https://tonyxuqaq.github.io/projects/DriveGPT4/"> DriveGPT4 </a></td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-RS</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-RS">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-RS">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-RS">🤗4B</a></td>
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<td> Adaptation for remote sensing domain </td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-Medical</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Medical">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Medical">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Medical">🤗4B</a></td>
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<td> Fine-tuning using our <a href="https://huggingface.co/datasets/OpenGVLab/InternVL-Domain-Adaptation-Data/blob/main/train_meta/internvl_1_2_finetune_medical.json">medical data</a>.</td>
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</tr>
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</table>
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The script for evaluation is in the [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#id3).
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## Training datasets
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- General domain dataset:
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ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN
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- Autonomous driving dataset:
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[DriveGPT4](https://tonyxuqaq.github.io/projects/DriveGPT4/).
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## Quick Start
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We provide an example code to run `Mini-InternVL2-4B` using `transformers`.
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> Please use transformers>=4.37.2 to ensure the model works normally.
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```python
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import numpy as np
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import torch
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import torchvision.transforms as T
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from decord import VideoReader, cpu
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from PIL import Image
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from torchvision.transforms.functional import InterpolationMode
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from transformers import AutoModel, AutoTokenizer
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def load_image(image_file, input_size=448, max_num=12):
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image = Image.open(image_file).convert('RGB')
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values
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# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
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path = 'OpenGVLab/Mini-InternVL2-4B-DA-BDD'
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True).eval().cuda()
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
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# set the max number of tiles in `max_num`
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pixel_values = load_image('path/to/image.jpg', max_num=12).to(torch.bfloat16).cuda()
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generation_config = dict(max_new_tokens=1024, do_sample=True)
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# pure-text conversation (纯文本对话)
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question = 'Hello, who are you?'
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response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'Can you tell me a story?'
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response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# single-image single-round conversation (单图单轮对话)
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question = '<image>\nPlease describe the image shortly.'
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response = model.chat(tokenizer, pixel_values, question, generation_config)
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print(f'User: {question}\nAssistant: {response}')
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# single-image multi-round conversation (单图多轮对话)
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question = '<image>\nPlease describe the image in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'Please write a poem according to the image.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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question = '<image>\nDescribe the two images in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'What are the similarities and differences between these two images.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
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question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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num_patches_list=num_patches_list,
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history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'What are the similarities and differences between these two images.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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num_patches_list=num_patches_list,
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history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# batch inference, single image per sample (单图批处理)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
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responses = model.batch_chat(tokenizer, pixel_values,
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num_patches_list=num_patches_list,
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questions=questions,
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generation_config=generation_config)
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for question, response in zip(questions, responses):
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print(f'User: {question}\nAssistant: {response}')
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```
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## Citation
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If you find this project useful in your research, please consider citing:
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```BibTeX
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@article{gao2024mini,
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title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
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author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
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journal={arXiv preprint arXiv:2410.16261},
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year={2024}
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}
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@article{chen2024expanding,
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title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
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author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
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journal={arXiv preprint arXiv:2412.05271},
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year={2024}
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}
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@article{chen2024far,
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title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
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author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
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journal={arXiv preprint arXiv:2404.16821},
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year={2024}
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}
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@inproceedings{chen2024internvl,
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title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
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author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={24185--24198},
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year={2024}
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}
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```
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