pollux435/mobilenet-v4-emotion

from transformers import AutoConfig
from modeling_mobilenet_v4_emotion import MobileNetV4EmotionClassifier
from preprocessing import preprocess_image
import torch

# 모델 로드
config = AutoConfig.from_pretrained("pollux435/mobilenet-v4-emotion", trust_remote_code=True)
model = MobileNetV4EmotionClassifier.from_pretrained("pollux435/mobilenet-v4-emotion", trust_remote_code=True)
model.eval()

# 이미지 예측
image_path = "your_image.jpg"
pixel_values = preprocess_image(image_path)

with torch.no_grad():
    outputs = model(pixel_values)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
predicted_class_id = outputs.logits.argmax().item()
predicted_emotion = config.id2label[str(predicted_class_id)]
confidence = probabilities[0][predicted_class_id].item()

print(f"예측된 감정: {predicted_emotion}")
print(f"신뢰도: {confidence:.4f}")

from transformers import AutoConfig
from modeling_mobilenet_v4_emotion import MobileNetV4EmotionClassifier

# 기존 모델 로드
model = MobileNetV4EmotionClassifier.from_pretrained("pollux435/mobilenet-v4-emotion", trust_remote_code=True)

# 새로운 클래스 수로 분류기 교체 (예: 3개 클래스)
import torch.nn as nn
in_features = model.classifier[-1].in_features
model.classifier[-1] = nn.Linear(in_features, 3)

# 이제 여러분의 데이터로 학습하세요!

pip install torch torchvision transformers timm pillow numpy

{
  "0": "angry",
  "1": "disgust", 
  "2": "fear",
  "3": "happy",
  "4": "sad",
  "5": "surprise",
  "6": "neutral"
}

@misc{mobilenetv4-emotion-2024,
  title={MobileNetV4 Based Emotion Recognition Model},
  author={pollux435},
  year={2024},
  publisher={Hugging Face},
  note={Trained on FER2013 dataset}
}