from transformers import AutoModel, AutoTokenizer
from PIL import Image
import torch

# Load model and tokenizer
model = AutoModel.from_pretrained("AoiNoGeso/japanese-resclip-v1", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("AoiNoGeso/japanese-resclip-v1", trust_remote_code=True)

# Load and process image
image = Image.open("path/to/image.jpg")
text = ["犬", "猫", "鳥"]

# Get features
image_features = model.get_image_features(**image)
text_features = model.get_text_features(**text)

# Compute similarity
similarity = torch.cosine_similarity(image_features, text_features)

Model Performance

Image Encoder: ResNet50 (fine-tuned from ImageNet pretrained weights)
Text Encoder: Frozen line-corporation/clip-japanese-base text encoder
Training Method: Knowledge Distillation Only (no contrastive learning)
Output Dimension: 512 (compatible with line-corporation/clip-japanese-base)

This model was trained using only knowledge distillation to mimic the image feature output of line-corporation/clip-japanese-base while using a ResNet50-based image encoder.