Update README.md

README.md

@@ -19,13 +19,13 @@ SqueezeNet: Very small but less accurate than the other options.
 Ultimately, EfficientNet-B0 was chosen due to its balance of accuracy and efficiency. While slightly larger than MobileNetV2, it was expected to fit within the free tier's resource limits and potentially provide better performance for this medical imaging task.
 Link : Efficientnet-b0
 
-#### Hyperparameter Choices:
+- #### Hyperparameter Choices:
 
-per_device_train_batch_size=32
-num_train_epochs=25
-fp16=True : Mixed precision training is enabled to leverage the GPU's Tensor Cores (if available) and speed up training.
-load_best_model_at_end=True : This ensures that we use the best performing model
-push_to_hub=True & hub_model_id & hub_strategy = "every_save"
+- - per_device_train_batch_size=32
+- - num_train_epochs=25
+- - fp16=True : Mixed precision training is enabled to leverage the GPU's Tensor Cores (if available) and speed up training.
+- - load_best_model_at_end=True : This ensures that we use the best performing model
+- - push_to_hub=True & hub_model_id & hub_strategy = "every_save"
 - 4. Preprocessing
 The dataset's images were already in a usable format (PIL). The preprocessing steps involved:
 Splitting: The dataset was split into training (80%) and validation (20%) sets.