Update README.md

README.md

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 ---
-license: mit
+license: apache-2.0
+tags:
+- image-classification
+datasets:
+- Face-Mask18K 
 ---
+
+# Distilled Data-efficient Image Transformer for Face Mask Detection
+
+Distilled data-efficient Image Transformer (DeiT) 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. 
+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.
+
+## Model description
+
+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.
+
+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.
+
+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).
+
+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.
+
+## Training Metrics
+    epoch                    =        0.89
+    total_flos               = 923776502GF
+    train_loss               =       0.057
+    train_runtime            =  0:40:10.40
+    train_samples_per_second =      23.943
+    train_steps_per_second   =       1.497
+---
+
+## Evaluation Metrics
+    epoch                   =       0.89
+    eval_accuracy           =     0.9894
+    eval_loss               =     0.0395
+    eval_runtime            = 0:00:36.81
+    eval_samples_per_second =     97.685
+    eval_steps_per_second   =     12.224