models google vit base patch16 224
The Vision Transformer (ViT) model, as introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al., underwent pre-training on ImageNet-21k with a resolution of 224x224. Subsequently, it was fine-tuned on ImageNet 2012, consisting of 1 million images and 1,000 classes, also at a resolution of 224x224. The model was first released in this repository, but the weights were converted to PyTorch from the timm repository by Ross Wightman, who had previously converted the weights from JAX to PyTorch.
An image is treated as a sequence of patches and it is processed by a standard Transformer encoder as used in NLP. These patches are linearly embedded, and a [CLS] token is added at the beginning of the sequence for classification tasks. The model also requires absolute position embeddings before feeding the sequence Transformer encoder. So the pre-training creates an inner representation of images that can be used to extract features that are useful for downstream tasks. For instance, if a dataset of labeled images is available, a linear layer can be placed on top of the pre-trained encoder, to train a standard classifier.
The ViT model is pre-trained on ImageNet-21k dataset with a resolution of 224x224 and fine-tuned on ImageNet 2012, consisting of 1 million images and 1,000 classes.
In the preprocessing step, images are resized to the same resolution 224x224. Then normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
The model was trained on TPUv3 hardware (8 cores). All models are trained using Adam with β1 = 0.9, β2 = 0.999, with a batch size of 4096, a high weight decay of 0.1, learning rate warmup of 10k steps. Authors found that it is beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224. For more details on hyperparameters refer to table 3 of the original-paper.
For more details on self-supervised pre-training (ImageNet-21k) followed by supervised fine-tuning (ImageNet-1k) refer to the section 3 and 4 of the original-paper.
For ViT image classification benchmark results, Refer to table 2 and table 5 of the original-paper.
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": ["image1", "image2"]
}Note: "image1" and "image2" string should be in base64 format or publicly accessible urls.
[
[
{
"label" : "can",
"score" : 0.91
},
{
"label" : "carton",
"score" : 0.09
},
],
[
{
"label" : "carton",
"score" : 0.9
},
{
"label" : "can",
"score" : 0.1
},
]
]/plot_google-vit-base-patch16-224_elephant_MC.png)
Version: 17
huggingface_model_id : google/vit-base-patch16-224 license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'true', 'apply_ort': 'true'}) task : image-classification hiddenlayerscanned training_dataset : imagenet-1k, imagenet-21k SharedComputeCapacityEnabled author : Google inference_compute_allow_list : ['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_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'] 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/google-vit-base-patch16-224/version/17
License: apache-2.0
SharedComputeCapacityEnabled: True
SHA: 2ddc9d4e473d7ba52128f0df4723e478fa14fb80
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|14|28
inference-recommended-sku: 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_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