Update README.md

README.md

@@ -1,3 +1,54 @@
 ---
-license: mit
+tags:
+- image-to-image
+library_name: keras
 ---
+## Model description
+This repo contains the model and the notebook [Image Classification using BigTransfer (BiT)](https://keras.io/examples/vision/bit/).
+
+Full credits go to [Sayan Nath](https://twitter.com/sayannath2350)
+
+Reproduced by [Rushi Chaudhari](https://github.com/rushic24)
+
+BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.
+
+## Dataset
+The [Flower Dataset](https://github.com/tensorflow/datasets/blob/master/docs/catalog/tf_flowers.md) is A large set of images of flowers
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+```
+RESIZE_TO = 384
+CROP_TO = 224
+BATCH_SIZE = 64
+STEPS_PER_EPOCH = 10
+AUTO = tf.data.AUTOTUNE  # optimise the pipeline performance
+NUM_CLASSES = 5  # number of classes
+SCHEDULE_LENGTH = (
+    500  # we will train on lower resolution images and will still attain good results
+)
+SCHEDULE_BOUNDARIES = [
+    200,
+    300,
+    400,
+]
+```
+
+The hyperparamteres like `SCHEDULE_LENGTH` and `SCHEDULE_BOUNDARIES` are determined based on empirical results. The method has been explained in the [original paper](https://arxiv.org/abs/1912.11370) and in their [Google AI Blog Post](https://ai.googleblog.com/2020/05/open-sourcing-bit-exploring-large-scale.html).
+
+The `SCHEDULE_LENGTH` is aslo determined whether to use [MixUp Augmentation](https://arxiv.org/abs/1710.09412) or not. You can also find an easy MixUp Implementation in [Keras Coding Examples](https://keras.io/examples/vision/mixup/).
+
+![](https://i.imgur.com/oSaIBYZ.jpeg)
+
+### Training results
+![](./metrics.png)
+
+<details>
+<summary> View Model Plot </summary>
+  ![Model Image](./model.png)
+  
+</details>