Update README.md

README.md

@@ -16,27 +16,20 @@ This model is an image classification model trained to identify different types
 
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-## Model Details
+# Model Details
 
 ### Model Description
 
 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:** Based on the notebook, this seems to be a personal project or tutorial. You should replace this with the actual developer's name or organization.
+* **Developed by:** `Recompense` Me!
 * **Model type:** Image Classification (likely Transfer Learning with a CNN backbone)
 * **Language(s) (NLP):** N/A (Image Classification)
 * **License:** MIT
 * **Finetuned from model:** EfficienntNetB0
 
-### Model Sources [optional]
 
-* **Repository:** \[More Information Needed - Link to the GitHub repository or other source code location]
-* **Paper [optional]:** \[More Information Needed - Link to any relevant paper if applicable]
-* **Demo [optional]:** \[More Information Needed - Link to a demo if available]
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-
-## Uses
+# Uses
 
 This model is intended for classifying images of food into 101 distinct categories. Potential use cases include:
 
@@ -46,7 +39,7 @@ This model is intended for classifying images of food into 101 distinct categori
 
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-## Limitations
+# Limitations
 
 * **Dataset Bias:** The model is trained on the Food101 dataset. Its performance may degrade on food images that are significantly different in style, presentation, or origin from those in the training data.
 * **Image Quality:** Performance can be affected by image quality, lighting conditions, occlusions, and variations in food presentation.
@@ -54,7 +47,7 @@ This model is intended for classifying images of food into 101 distinct categori
 
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-## Evaluation
+# Evaluation
 
 The model's performance was evaluated using standard classification metrics on a validation set from the Food101 dataset.
 
@@ -91,7 +84,7 @@ Transfer learning helped the model achieve greater accuracy, though the model st
 
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-## Environmental Impact
+# Environmental Impact
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
@@ -103,7 +96,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
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-## Technical Specifications
+# Technical Specifications
 
 ### Model Architecture and Objective
 
@@ -124,7 +117,7 @@ The model was trained using a Tesla T4 GPU on Google Cloud in the us-central reg
 
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-## Usage
+# Usage
 
 Here's an example of how to use the model for inference on a new image. This assumes the model has been saved in a TensorFlow SavedModel format.