Update README.md

README.md

@@ -23,7 +23,7 @@ This model is an image classification model trained to identify different types
 This model is a deep learning model for classifying food images into one of 101 categories from the Food101 dataset. It was trained using TensorFlow and likely employs a transfer learning approach, leveraging the features learned by a model pre-trained on a large dataset like ImageNet. The training process included the use of mixed precision for potentially faster training and reduced memory usage.
 
 * **Developed by:** `Recompense` Me!
-* **Model type:** Image Classification (likely Transfer Learning with a CNN backbone)
+* **Model type:** Image Classification (Transfer Learning with a CNN backbone)
 * **Language(s) (NLP):** N/A (Image Classification)
 * **License:** MIT
 * **Finetuned from model:** EfficienntNetB0
@@ -101,7 +101,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Model Architecture and Objective
 
-The model is likely a fine-tuned convolutional neural network (CNN) classifier. The notebook mentions using mixed precision training, which suggests a modern CNN architecture compatible with `float16` data types. The objective is to minimize the classification loss (e.g., categorical cross-entropy) to accurately predict the food category given an image.
+The model is  a fine-tuned convolutional neural network (CNN) classifier.Mixed precision training was used for faster training, a modern CNN architecture compatible with `float16` data types. The objective is to minimize the classification loss (e.g., categorical cross-entropy) to accurately predict the food category given an image.
 
 ### Compute Infrastructure
 
@@ -114,7 +114,7 @@ The model was trained using a Tesla T4 GPU on Google Cloud in the us-central reg
 * NumPy
 * Matplotlib
 * Scikit-learn
-* Helper functions from `helper_functions.py` (likely for plotting, data handling)
+* Helper functions from `helper_functions.py` (for plotting, data handling)
 
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