Trained a vit model to do classification on anime dataset.

Divided into four categories: head_only, upperbody, knee_level, fullbody
+ head_only
![head_only_example2.jpg](https://cdn-uploads.huggingface.co/production/uploads/63891deed68e37abd59e883f/0fbMrqDA8_PKrm9UG2cR3.jpeg)
+ upperbody
![upperbody_example2.jpg](https://cdn-uploads.huggingface.co/production/uploads/63891deed68e37abd59e883f/rxEXJhuJLHrulgHyaRy61.jpeg)
+ knee_level
![knee_level_example2.jpg](https://cdn-uploads.huggingface.co/production/uploads/63891deed68e37abd59e883f/o63VCshR6u1d_p2myxum9.jpeg)
+ fullbody
![fullbody_example2.jpg](https://cdn-uploads.huggingface.co/production/uploads/63891deed68e37abd59e883f/UQ4UKrko4qcubo0ueM0wq.jpeg)
```
from datasets import load_dataset
from PIL import Image
from transformers import ViTImageProcessor, ViTForImageClassification, TrainingArguments, Trainer
import torch
import numpy as np
from datasets import load_metric
import os
import shutil

model_name_or_path = 'lrzjason/anime_portrait_vit'
image_processor = ViTImageProcessor.from_pretrained(model_name_or_path)
model = ViTForImageClassification.from_pretrained(model_name_or_path)

input_dir = '/path/to/dir'
file = 'example.jpg'
image = Image.open(os.path.join(input_dir, file))

inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(f'predicted_label: {model.config.id2label[predicted_label]}')
```

Using this dataset:
https://huggingface.co/datasets/animelover/genshin-impact-images
{"license": "apache-2.0", "tags": ["anime"]}
transformers safetensors vit image-classification anime license:apache-2.0 autotrain_compatible endpoints_compatible region:us