一、DINOv2 模型简介及使用
DINOv2是由Meta AI开发的第二代自监督视觉变换器模型，采用 Vision Transformer (ViT) 架构 。其核心特点是在无需人工标签的情况下，通过自监督学习技术，从海量无标注图像中学习有意义的视觉特征表示，类似于 NLP 领域的自监督 Base 模型，DINOv2 已经具有了对图像的理解能力，和强大的图像特征提取能力，因此它可以作为几乎所有计算机视觉任务的骨干模型。
下面是官方演示地址：
https://dinov2.metademolab.com/demos?category=segmentation
深度估计效果：
语义分割效果：
GitHub 开源地址：
https://github.com/facebookresearch/dinov2
huggingface 模型地址：
https://huggingface.co/facebook/dinov2-base/tree/main
本文借助 DINOv2 强大的特征提取能力，实现图图相似度检索任务，但开始前，首先 先了解一下如何基于 DINOv2 实现图像的相似度计算：
from transformers import AutoImageProcessor, AutoModel
from PIL import Image
import matplotlib.pyplot as plt
import torch
plt.rcParams['font.sans-serif'] = ['SimHei']
# 生成图像特征
def gen_image_features(processor, model, device, image):with torch.no_grad():inputs = processor(images=image, return_tensors="pt").to(device)outputs = model(**inputs)image_features = outputs.last_hidden_stateimage_features = image_features.mean(dim=1)return image_features[0]# 计算两个图像的相似度
def similarity_image(processor, model, device, image1, image2):features1 = gen_image_features(processor, model, device, image1)features2 = gen_image_features(processor, model, device, image2)cos_sim = torch.cosine_similarity(features1, features2, dim=0)cos_sim = (cos_sim + 1) / 2return cos_sim.item()def main():model_dir = "facebook/dinov2-base"processor = AutoImageProcessor.from_pretrained(model_dir)model = AutoModel.from_pretrained(model_dir)device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")model.to(device)image1 = Image.open("img/dog1.jpg")image2 = Image.open("img/dog2.jpg")similarity = similarity_image(processor, model, device, image1, image2)plt.figure()plt.axis('off')plt.title(f"相似度: {similarity}")plt.subplot(1, 2, 1)plt.imshow(image1)plt.subplot(1, 2, 2)plt.imshow(image2)plt.show()if __name__ == '__main__':main()
相似度计算的主要核心就是基于 DINOv2 生成特征向量，图图相似度检索也依赖这一点，不过需要多出一个特征向量的持久化存储端，整体实现架构如下图所示，其中特征向量存储采用 Milvus 数据库。
被检索图像数据集特征提取过程：
图像相似度检索过程：
关于 Milvus 的使用，可以参考下面这篇博客：
Milvus 向量数据库介绍及使用
二、图图相似度检索实现 - 图像特征持久化
首先准备图像数据集，这里我随便准备了几张猫和狗的图片：
创建 Milvus Collection，其中 DINOv2 特征向量维度为 768 维：
from pymilvus import MilvusClient, DataTypeclient = MilvusClient("http://192.168.0.5:19530")schema = MilvusClient.create_schema(auto_id=True,enable_dynamic_field=False,
)
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=768)
schema.add_field(field_name="image_name", datatype=DataType.VARCHAR, max_length=256)
schema.verify()
index_params = client.prepare_index_params()
index_params.add_index(field_name="id",index_type="STL_SORT"
)
index_params.add_index(field_name="vector",index_type="IVF_FLAT",metric_type="L2",params={"nlist": 1024}
)
# 创建 collection
client.create_collection(collection_name="dinov2_collection",schema=schema,index_params=index_params
)
图像数据集提取特征向量后，持久化到 Milvus 中：
from transformers import AutoImageProcessor, AutoModel
from PIL import Image
from pymilvus import MilvusClient
from tqdm import tqdm
import torch
import os# 生成特征向量
def gen_image_features(processor, model, device, image):with torch.no_grad():inputs = processor(images=image, return_tensors="pt").to(device)outputs = model(**inputs)image_features = outputs.last_hidden_stateimage_features = image_features.mean(dim=1)return image_features[0]def main():# 创建Milvus客户端client = MilvusClient("http://192.168.0.5:19530")# 加载模型model_dir = "facebook/dinov2-base"processor = AutoImageProcessor.from_pretrained(model_dir)model = AutoModel.from_pretrained(model_dir)device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")model.to(device)# 读取数据集dataset_path = "./img"for image_name in tqdm(os.listdir(dataset_path)):image_path = os.path.join(dataset_path, image_name)image = Image.open(image_path)# 提取特征向量features = gen_image_features(processor, model, device, image)# 存吃至 milvusclient.insert(collection_name="dinov2_collection",data={"vector": features,"image_name": image_name})if __name__ == '__main__':main()
运行后，在 Milvus insight 工具中，可以看到存储的内容：
三、图图相似度检索实现 - 图像特征检索
检索就是拿着当前图像的特征，去向量数据库中检索相似的特征信息，实现过程如下：
from transformers import AutoImageProcessor, AutoModel
from PIL import Image
from pymilvus import MilvusClient
import matplotlib.pyplot as plt
import torch
import os# 生成特征向量
def gen_image_features(processor, model, device, image):with torch.no_grad():inputs = processor(images=image, return_tensors="pt").to(device)outputs = model(**inputs)image_features = outputs.last_hidden_stateimage_features = image_features.mean(dim=1)return image_features[0].tolist()def main():# 创建Milvus客户端client = MilvusClient("http://192.168.0.5:19530")# 加载模型model_dir = "facebook/dinov2-base"processor = AutoImageProcessor.from_pretrained(model_dir)model = AutoModel.from_pretrained(model_dir)device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")model.to(device)# 检索图像, 采用不在Milvus数据集中的图像image = Image.open("./img2/dog.jpeg")# 提取特征向量features = gen_image_features(processor, model, device, image)# 特征召回results = client.search(collection_name="dinov2_collection",data=[features],limit=2,output_fields=["image_name"],search_params={"metric_type": "L2","params": {}})plt.figure()plt.axis('off')for i, res in enumerate(results[0]):image_name = res["entity"]["image_name"]image_path = os.path.join("./img", image_name)image = Image.open(image_path)plt.subplot(1, 2, (i+1))plt.imshow(image)plt.show()if __name__ == '__main__':main()测试输入检索图像：
召回图像：
测试输入检索图像：
召回图像：
