---
library_name: transformers
tags:
- vision
- anime
- image-feature-extraction
---

# ViTMAE (base-sized model) pre-trained on Pixiv

ViTMAE model pre-trained on Pixiv artworks from id 20 to 100649536. Architecture is the same as [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base), but with a smaller patch size (14) and a larger image size (266).

All training was done on TPUs sponsored by [TPU Research Cloud](https://sites.research.google/trc/about/).

## Usage

```
from transformers import AutoImageProcessor, ViTMAEForPreTraining, ViTModel

# for resizing images to 266 pixes and normalizing to [-1, 1]
processor = AutoImageProcessor.from_pretrained("zapparias/pixiv-vit-mae-base")

# load encoder + decoder
model = ViTMAEForPreTraining.from_pretrained("zapparias/pixiv-vit-mae-base")

# you can also load the encoder into a standard ViT model for feature extraction
model = ViTModel.from_pretrained("zapparias/pixiv-vit-mae-base", add_pooling_layer=False)
```