Update README.md

README.md

@@ -28,19 +28,16 @@ It is trained on the FER2013and AffectNet datasets, which consist of facial imag
 ## Model Details
 The model has been fine-tuned using the following hyperparameters:
 
-train batch size: 32
+| Hyperparameter          | Value      |
+|-------------------------|------------|
+| Train Batch Size        | 32         |
+| Eval Batch Size         | 64         |
+| Learning Rate           | 2e-4       |
+| Gradient Accumulation   | 2          |
+| LR Scheduler            | Linear     |
+| Warmup Ratio            | 0.04       |
+| Num Epochs              | 10         |
 
-eval batch size: 64
-
-learning rate:2e-4
-
-gradient sccumulation steps:2
-
-lr scheduler:'linear'
-
-warmup ratio:0.04
-
-num epochs:10
 
 
 
@@ -54,7 +51,8 @@ num epochs:10
 ## How to Get Started with the Model
 Example usage:
 
-'''from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
+'''python 
+from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
 
 pipe = pipeline("image-classification", model="HardlyHumans/Facial-expression-detection")
 
@@ -67,7 +65,8 @@ outputs = model(**inputs)
 
 logits = outputs.logits
 predicted_class_idx = logits.argmax(-1).item()
-predicted_label = labels[predicted_class_idx]'''
+predicted_label = labels[predicted_class_idx]
+'''