Package: dreamsim
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic DataProject Page|Paper|BibtexStephanie Fu* $^{1}$,Netanel Tamir* $^{2}$,Shobhita Sundaram* $^{1}$,Lucy Chai$^1$,Richard Zhang$^3$,Tali Dekel$^2$,Phillip Isola$^1$. (*equal contribution)$^1$ MIT, $^2$ Weizmann Institute of Science, $^3$ Adobe Research.SummaryCurrent metrics for perceptual image similarity operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture mid-level differences in layout, pose, semantic content, etc. Models that use image-level embeddings such as DINO and CLIP capture high-level and semantic judgements, but may not be aligned with human perception of more finegrained attributes.DreamSim is a new metric for perceptual image similarity that bridges the gap between "low-level" metrics (e.g. LPIPS, PSNR, SSIM) and "high-level" measures (e.g. CLIP). Our model was trained by concatenating CLIP, OpenCLIP, and DINO embeddings, and then finetuning on human perceptual judgements. We gathered these judgements on a dataset of ~20k image triplets, generated by diffusion models. Our model achieves better alignment with human similarity judgements than existing metrics, and can be used for downstream applications such as image retrieval.RequirementsLinuxPython 3NVIDIA GPU + CUDA CuDNN (DreamSim is only supported on CUDA devices)SetupOption 1:Install using pip:pip install dreamsimThe package is used for importing and using the DreamSim model.Option 2:Clone our repo and install dependencies.
This is necessary for running our training/evaluation scripts.python3 -m venv ds
source ds/bin/activate
pip install -r requirements.txt
export PYTHONPATH="$PYTHONPATH:$(realpath ./dreamsim)"To install with conda:conda create -n ds
conda activate ds
conda install pip # verify with the `which pip` command
pip install -r requirements.txt
export PYTHONPATH="$PYTHONPATH:$(realpath ./dreamsim)"UsageFor walk-through examples of the below use-cases, check out ourColab demo.Quickstart: Perceptual similarity metricThe basic use case is to measure the perceptual distance between two images.A higher score means more different, lower means more similar.The following code snippet is all you need. The first time that you rundreamsimit will automatically download the model weights. The default model settings are specified in./dreamsim/config.py.from dreamsim import dreamsim
from PIL import Image

model, preprocess = dreamsim(pretrained=True)

img1 = preprocess(Image.open("img1_path")).to("cuda")
img2 = preprocess(Image.open("img2_path")).to("cuda")
distance = model(img1, img2) # The model takes an RGB image from [0, 1], size batch_sizex3x224x224To run on example images, rundemo.py. The script should produce distances (0.424, 0.34).(new!) Single-branch modelsBy default, DreamSim uses an ensemble of CLIP, DINO, and OpenCLIP (all ViT-B/16). If you need a lighter-weight model you can usesingle-branchversions of DreamSim where only a single backbone is finetuned. The available options are OpenCLIP-ViTB/32, DINO-ViTB/16, CLIP-ViTB/32, in order of performance.To load a single-branch model, use thedreamsim_typeargument. For example:dreamsim_dino_model, preprocess = dreamsim(pretrained=True, dreamsim_type="dino_vitb16")Feature extractionTo extract asingle image embeddingusing dreamsim, use theembedmethod as shown in the following snippet:img1 = preprocess(Image.open("img1_path")).to("cuda")
embedding = model.embed(img1)The perceptual distance between two images is the cosine distance between their embeddings. If the embeddings are normalized (true by default) L2 distance can also be used.Image retrievalOur model can be used for image retrieval, and plugged into existing such pipelines. The code below ranks a dataset of images based on their similarity to a given query image.To speed things up, instead of directly callingmodel(query, image)for each pair, we use themodel.embed(image)method to pre-compute single-image embeddings, and then take the cosine distance between embedding pairs.import pandas as pd
from tqdm import tqdm
import torch.nn.functional as F

# let query be a sample image.
# let images be a list of images we are searching.

# Compute the query image embedding
query_embed = model.embed(preprocess(query).to("cuda"))
dists = {}

# Compute the (cosine) distance between the query and each search image
for i, im in tqdm(enumerate(images), total=len(images)):
   img_embed = model.embed(preprocess(im).to("cuda"))
   dists[i] = (1 - F.cosine_similarity(query_embed, img_embed, dim=-1)).item()

# Return results sorted by distance
df = pd.DataFrame({"ids": list(dists.keys()), "dists": list(dists.values())})
return df.sort_values(by="dists")Perceptual loss functionOur model can be used as a loss function for iterative optimization (similarly to the LPIPS metric). These are the key lines; for the full example, refer to theColab.for i in range(n_iters):
    dist = model(predicted_image, reference_image)
    dist.backward()
    optimizer.step()CitationIf you find our work or any of our materials useful, please cite our paper:@misc{fu2023dreamsim,
      title={DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data}, 
      author={Stephanie Fu and Netanel Tamir and Shobhita Sundaram and Lucy Chai and Richard Zhang and Tali Dekel and Phillip Isola},
      year={2023},
      eprint={2306.09344},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}AcknowledgementsOur code borrows from the"Deep ViT Features as Dense Visual Descriptors"repository for ViT feature extraction, and takes inspiration from theUniverSegrespository for code structure.