metadata
language:
- en
base_model:
- openai/clip-vit-large-patch14
tags:
- emotion_prediction
- VEA
- computer_vision
- perceptual_tasks
- CLIP
- EmoSet
PreceptCLIP-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 with an additional MLP head. 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
Usage
To use the model for inference:
from torchvision import transforms
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model
model_path = hf_hub_download(repo_id="PerceptCLIP/PerceptCLIP_Emotions", filename="perceptCLIP_Emotions.pth")
model = torch.load(model_path).to(device).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 the class index
# Get emotion label
predicted_emotion = idx2label[predicted.item()]
print(f"Predicted Emotion: {predicted_emotion}")