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---
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license: apache-2.0
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tags:
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- image-classification
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datasets:
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- Face-Mask18K
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---
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# Vision Transformer (ViT) for Face Mask Detection
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Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al.
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Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al.
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## Model description
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The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
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Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), 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.
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Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
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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.
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## Training Metrics
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epoch = 0.89
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total_flos = 923776502GF
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train_loss = 0.057
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train_runtime = 0:40:10.40
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train_samples_per_second = 23.943
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train_steps_per_second = 1.497
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---
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## Evaluation Metrics
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epoch = 0.89
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eval_accuracy = 0.9894
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eval_loss = 0.0395
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eval_runtime = 0:00:36.81
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eval_samples_per_second = 97.685
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eval_steps_per_second = 12.224 |
