models facebook dinov2 base imagenet1k 1 layer - Azure/azureml-assets GitHub Wiki
Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository.
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.\n Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n Note that this model does not include any fine-tuned heads.\n 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.
For more details on Dinov2, Review the original-paper and the model's github repo
The model takes an image as input and returns a class token and patch tokens, and optionally 4 register tokens.
The embedding dimension is:
The models follow a Transformer architecture, with a patch size of 14. In the case of registers, we add 4 register tokens, learned during training, to the input sequence after the patch embedding.
For a 224x224 image, this results in 1 class token + 256 patch tokens, and optionally 4 register tokens.
The models can accept larger images provided the image shapes are multiples of the patch size (14). If this condition is not verified, the model will crop to the closest smaller multiple of the patch size.
The Dinov2 model is pre-trained and fine-tuned on ImageNet 2012, of 1 million consistingimages and 1,000 classes on a resolution of 224x224.
apache-2.0
| Inference type | Python sample (Notebook) | CLI with YAML |
|---|---|---|
| Real time | image-classification-online-endpoint.ipynb | image-classification-online-endpoint.sh |
| Batch | image-classification-batch-endpoint.ipynb | image-classification-batch-endpoint.sh |
| Task | Use case | Dataset | Python sample (Notebook) | CLI with YAML |
|---|---|---|---|---|
| Image Multi-class classification | Image Multi-class classification | fridgeObjects | fridgeobjects-multiclass-classification.ipynb | fridgeobjects-multiclass-classification.sh |
| Image Multi-label classification | Image Multi-label classification | multilabel fridgeObjects | fridgeobjects-multilabel-classification.ipynb | fridgeobjects-multilabel-classification.sh |
| Task | Use case | Dataset | Python sample (Notebook) |
|---|---|---|---|
| Image Multi-class classification | Image Multi-class classification | fridgeObjects | image-multiclass-classification.ipynb |
| Image Multi-label classification | Image Multi-label classification | multilabel fridgeObjects | image-multilabel-classification.ipynb |
{
"input_data": {
"columns": [
"image"
],
"index": [0, 1],
"data": ["image1", "image2"]
}
}Note: "image1" and "image2" string should be in base64 format or publicly accessible urls.
[
{
"probs": [0.91, 0.09],
"labels": ["can", "carton"]
},
{
"probs": [0.1, 0.9],
"labels": ["can", "carton"]
}
]/plot_facebook-dinov2-base-imagenet1k-1-layer_elephant_MC.png)
Version: 2
huggingface_model_id : facebook/dinov2-base-imagenet1k-1-layer training_dataset : imagenet-1k SharedComputeCapacityEnabled author : Meta license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'true', 'apply_ort': 'true'}) task : image-classification inference_compute_allow_list : ['Standard_DS2_v2', 'Standard_D2a_v4', 'Standard_D2as_v4', 'Standard_DS3_v2', 'Standard_D4a_v4', 'Standard_D4as_v4', 'Standard_DS4_v2', 'Standard_D8a_v4', 'Standard_D8as_v4', 'Standard_DS5_v2', 'Standard_D16a_v4', 'Standard_D16as_v4', 'Standard_D32a_v4', 'Standard_D32as_v4', 'Standard_D48a_v4', 'Standard_D48as_v4', 'Standard_D64a_v4', 'Standard_D64as_v4', 'Standard_D96a_v4', 'Standard_D96as_v4', 'Standard_F4s_v2', 'Standard_FX4mds', 'Standard_F8s_v2', 'Standard_FX12mds', 'Standard_F16s_v2', 'Standard_F32s_v2', 'Standard_F48s_v2', 'Standard_F64s_v2', 'Standard_F72s_v2', 'Standard_FX24mds', 'Standard_FX36mds', 'Standard_FX48mds', 'Standard_E2s_v3', 'Standard_E4s_v3', 'Standard_E8s_v3', 'Standard_E16s_v3', 'Standard_E48s_v3', 'Standard_E32s_v3', 'Standard_NC4as_T4_v3', 'Standard_E64s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC48ads_A100_v4', 'Standard_NC24ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND40rs_v2', 'Standard_ND96amsr_A100_v4'] evaluation_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2'] finetune_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']
View in Studio: https://ml.azure.com/registries/azureml/models/facebook-dinov2-base-imagenet1k-1-layer/version/2
License: apache-2.0
SharedComputeCapacityEnabled: True
SHA: 3ec10e6c76362191b61260300fe1d6173a8dd7e1
finetuning-tasks: image-classification
finetune-min-sku-spec: 4|1|28|176
finetune-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2
evaluation-min-sku-spec: 4|1|28|176
evaluation-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2
inference-min-sku-spec: 2|0|7|14
inference-recommended-sku: Standard_DS2_v2, Standard_D2a_v4, Standard_D2as_v4, Standard_DS3_v2, Standard_D4a_v4, Standard_D4as_v4, Standard_DS4_v2, Standard_D8a_v4, Standard_D8as_v4, Standard_DS5_v2, Standard_D16a_v4, Standard_D16as_v4, Standard_D32a_v4, Standard_D32as_v4, Standard_D48a_v4, Standard_D48as_v4, Standard_D64a_v4, Standard_D64as_v4, Standard_D96a_v4, Standard_D96as_v4, Standard_F4s_v2, Standard_FX4mds, Standard_F8s_v2, Standard_FX12mds, Standard_F16s_v2, Standard_F32s_v2, Standard_F48s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_FX24mds, Standard_FX36mds, Standard_FX48mds, Standard_E2s_v3, Standard_E4s_v3, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E48s_v3, Standard_E64s_v3, Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2