Update README.md

README.md

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+language: 
+  - en
+
+thumbnail:
+
+tags:
+- classification
+- EANet
+- keras
+- TensorFlow
+
+library_name: generic
+libraries: TensorBoard
+license: apache-2.0
+metrics:
+- accuracy
+model-index:
+- name: Image-Classification-using-EANet
+  results:
+  - task: 
+      type: Image-Classification-using-EANet
+    dataset:
+      type: Image
+      name: CIFAR100
+    metrics:
+    - type: accuracy
+      value: []
+    - type: validation loss
+      value: [] 
+
+
+**Introduction**
 This example implements the EANet model for image classification, and demonstrates it on the CIFAR-100 dataset. EANet introduces a novel attention mechanism named external attention, based on two external, small, learnable, and shared memories, which can be implemented easily by simply using two cascaded linear layers and two normalization layers. It conveniently replaces self-attention as used in existing architectures. External attention has linear complexity, as it only implicitly considers the correlations between all samples.