models facebook deit base patch16 224

facebook-deit-base-patch16-224

Overview

This model is a more efficiently trained Vision Transformer (ViT). The Vision Transformer (ViT) is a transformer encoder model that is pre-trained and fine-tuned on a large collection of images in a supervised fashion. It is presented with images as sequences of fixed-size patches, which are linearly embedded, and before feeding the sequence to the layers of the Transformer encoder, absolute position embeddings are added. By pre-training the model, it is able to generate an inner representation of images that can be used to extract useful features for downstream tasks. For example, if one has a dataset of labeled images, a standard classifier can be trained by placing a linear layer on top of the pre-trained encoder. The last hidden state of the [CLS] token can be used as a representation of the entire image.

The above summary was generated using ChatGPT. Review the original-model-card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model.

Inference samples

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

Finetuning samples

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

Model Evaluation

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

Sample inputs and outputs (for real-time inference)

Sample input

{
  "input_data": {
    "columns": [
      "image"
    ],
    "index": [0, 1],
    "data": ["image1", "image2"]
  }
}

Note: "image1" and "image2" string should be in base64 format or publicly accessible urls.

Sample output

[
    {
        "probs": [0.91, 0.09],
        "labels": ["can", "carton"]
    },
    {
        "probs": [0.1, 0.9],
        "labels": ["can", "carton"]
    }
]

Model inference - visualization for a sample image

Version: 9

Tags

Preview license : apache-2.0 model_specific_defaults : ordereddict([('apply_deepspeed', 'true'), ('apply_ort', 'true')]) task : image-classification

View in Studio: https://ml.azure.com/registries/azureml/models/facebook-deit-base-patch16-224/version/9

License: apache-2.0

Properties

SHA: fb2c78a54a5637dec350432794f7b93e31f910c9

datasets: imagenet-1k

evaluation-min-sku-spec: 4|1|28|176

evaluation-recommended-sku: Standard_NC6s_v3

finetune-min-sku-spec: 4|1|28|176

finetune-recommended-sku: Standard_NC6s_v3

finetuning-tasks: image-classification

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

model_id: facebook/deit-base-patch16-224