facebook
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dinov2-with-registers-base-imagenet1k-1-layer

@@ -1,199 +1,81 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+pipeline_tag: image-classification
+license: apache-2.0
+tags:
+- dino
+- vision
+inference: false
 ---
+# Vision Transformer (base-sized model) trained using DINOv2, with registers
+Vision Transformer (ViT) model introduced in the paper [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588) by Darcet et al. and first released in [this repository](https://github.com/facebookresearch/dinov2).
+Disclaimer: The team releasing DINOv2 with registers did not write a model card for this model so this model card has been written by the Hugging Face team.
+## Model description
+The Vision Transformer (ViT) is a transformer encoder model (BERT-like) [originally introduced](https://arxiv.org/abs/2010.11929) to do supervised image classification on ImageNet.
+Next, people figured out ways to make ViT work really well on self-supervised image feature extraction (i.e. learning meaningful features, also called embeddings) on
+images without requiring any labels. Some example papers here include [DINOv2](https://huggingface.co/papers/2304.07193) and [MAE](https://arxiv.org/abs/2111.06377).
+The authors of DINOv2 noticed that ViTs have artifacts in attention maps. It’s due to the model using some image patches as “registers”. The authors propose a fix: just add some new tokens (called "register" tokens), which you only use during pre-training (and throw away afterwards). This results in:
+- no artifacts
+- interpretable attention maps
+- and improved performances.
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dinov2_with_registers_visualization.png"
+alt="drawing" width="600"/>
+<small> Visualization of attention maps of various models trained with vs. without registers. Taken from the <a href="https://arxiv.org/abs/2309.16588">original paper</a>. </small>
+Note that this model does not include any fine-tuned heads.
+By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
+## Intended uses & limitations
+You can use the raw model to classify an image into one of the 1000 possible ImageNet classes. See the [model hub](https://huggingface.co/models?other=dinov2_with_registers) to look for
+fine-tuned versions on a task that interests you.
+### How to use
+Here is how to use this model:
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+from PIL import Image
+import requests
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+processor = AutoImageProcessor.from_pretrained('facebook/dinov2-with-registers-base-imagenet1k-1-layer')
+model = AutoModelForImageClassification.from_pretrained('facebook/dinov2-with-registers-base-imagenet1k-1-layer')
+inputs = processor(images=image, return_tensors="pt")
+with torch.no_grad():
+  outputs = model(**inputs)
+  logits = outputs.logits
+class_idx = outputs.logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[class_idx])
+```
+### BibTeX entry and citation info
+```bibtex
+@misc{darcet2024visiontransformersneedregisters,
+      title={Vision Transformers Need Registers},
+      author={Timothée Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
+      year={2024},
+      eprint={2309.16588},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2309.16588},
+}
+```