metadata
language:
- en
base_model:
- openai/clip-vit-large-patch14
tags:
- memorability
- computer_vision
- perceptual_tasks
- CLIP
- LaMem
- THINGS
PerceptCLIP-Memorability is a model designed to predict image memorability (the likelihood of an image to be remembered). This is the official model from the paper:
📄 "Don't Judge Before You CLIP: A Unified Approach for Perceptual Tasks".
We apply LoRA adaptation on the CLIP visual encoder and add an MLP head for memorability prediction. Our model achieves state-of-the-art results.
Training Details
- Dataset: LaMem (Large-Scale Image Memorability)
- Architecture: CLIP Vision Encoder (ViT-L/14) with LoRA adaptation
- Loss Function: Mean Squared Error (MSE) Loss for memorability prediction
- Optimizer: AdamW
- Learning Rate: 5e-05
- Batch Size: 32
Installation & Requirements
You can set up the environment using environment.yml or manually install dependencies:
- python=3.9.15
- cudatoolkit=11.7
- torchvision=0.14.0
- transformers=4.45.2
- peft=0.14.0
Usage
To use the model for inference:
from torchvision import transforms
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
import importlib.util
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the model class definition dynamically
class_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="modeling.py")
spec = importlib.util.spec_from_file_location("modeling", class_path)
modeling = importlib.util.module_from_spec(spec)
spec.loader.exec_module(modeling)
# initialize a model
ModelClass = modeling.clip_lora_model
model = ModelClass().to(device)
# Load pretrained model
model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Memorability", filename="perceptCLIP_Memorability.pth")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Load an image
image = Image.open("image_path.jpg").convert("RGB")
# Preprocess and predict
def Mem_preprocess():
transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(size=(224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711))
])
return transform
image = Mem_preprocess()(image).unsqueeze(0).to(device)
with torch.no_grad():
mem_score = model(image).item()
print(f"Predicted Memorability Score: {mem_score:.4f}")
Citation
If you use this model in your research, please cite:
@article{zalcher2025don,
title={Don't Judge Before You CLIP: A Unified Approach for Perceptual Tasks},
author={Zalcher, Amit and Wasserman, Navve and Beliy, Roman and Heinimann, Oliver and Irani, Michal},
journal={arXiv preprint arXiv:2503.13260},
year={2025}
}