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PerceptCLIP/
PerceptCLIP_Emotions

openai/clip-vit-large-patch14

finetune

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PerceptCLIP-Emotions is a model designed to predict the emotions that an image evokes in users. 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 emotion classification. Our model achieves state-of-the-art results.

Training Details

Dataset: EmoSet
Architecture: CLIP Vision Encoder (ViT-L/14) with LoRA adaptation
Loss Function: Cross Entropy Loss
Optimizer: AdamW
Learning Rate: 0.0001
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:

python

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_Emotions", 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_Emotions", filename="perceptCLIP_Emotions.pth")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()

# Emotion label mapping
idx2label = {
    0: "amusement",
    1: "awe",
    2: "contentment",
    3: "excitement",
    4: "anger",
    5: "disgust",
    6: "fear",
    7: "sadness"
}

# Preprocessing function
def emo_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

# Load an image
image = Image.open("image_path.jpg").convert("RGB")
image = emo_preprocess()(image).unsqueeze(0).to(device)

# Run inference
with torch.no_grad():
    outputs = model(image)
    _, predicted = outputs.max(1)

# Get emotion label
predicted_emotion = idx2label[predicted.item()]
print(f"Predicted Emotion: {predicted_emotion}")

Citation

If you use this model in your research, please cite:

bibtex

@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}
}