Update README.md

README.md

@@ -9,11 +9,13 @@ base_model:
 pipeline_tag: image-classification
 ---
 
+
 # Model Card for Food Vision Model
 
 This model is an image classification model trained to identify different types of food from images. It was developed as part of a Food Vision project, likely utilizing transfer learning on a pre-trained convolutional neural network.
 
 ---
+
 ## Model Details
 
 ### Model Description
@@ -21,35 +23,37 @@ 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:** 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.
-
 * **Model type:** Image Classification (likely Transfer Learning with a CNN backbone)
-
 * **Language(s) (NLP):** N/A (Image Classification)
-
 * **License:** MIT
-
 * **Finetuned from model:** EfficienntNetB0
 
-### Uses
+### 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]
+
+---
+
+## Uses
 
 This model is intended for classifying images of food into 101 distinct categories. Potential use cases include:
 
 * Food recognition in mobile applications.
-
 * Organizing food images in databases.
-
 * Assisting in dietary tracking or recipe suggestions based on images.
 
-
 ---
+
 ## 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.
-
 * **Specificity:** While it classifies into 101 categories, it may not distinguish between very similar dishes or variations within a category.
 
+---
+
 ## Evaluation
 
 The model's performance was evaluated using standard classification metrics on a validation set from the Food101 dataset.
@@ -59,7 +63,6 @@ The model's performance was evaluated using standard classification metrics on a
 The model was evaluated on the validation split of the Food101 dataset.
 
 * **Food101 Dataset:** A dataset of 101 food categories, with 101,000 images. 750 training images and 250 testing images per class.
-
 * **Source:** [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/food101)
 
 #### Factors
@@ -72,36 +75,34 @@ The primary evaluation metric used is Accuracy. A confusion matrix was also gene
 
 * **Accuracy:** The proportion of correctly classified images out of the total number of images evaluated.
 
-  $$
-  \text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}
-  $$
+    $$
+    \text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}
+    $$
 
 * **Confusion Matrix:** A table that visualizes the performance of a classification model. Each row represents the instances in an actual class, while each column represents the instances in a predicted class.
 
 ### Results
 
-70-80% Fluctualting accuracy on validation data
+70-80% Fluctuating accuracy on validation data
 
 #### Summary
 
-Transfer learning helped the model achieve greater accuracy, though the model struggled with food closely related to each other indicating more data was needed. The Dataset used alot but more data is still needed to differentiate between closely looking food.
+Transfer learning helped the model achieve greater accuracy, though the model struggled with food closely related to each other indicating more data was needed. The Dataset used a lot but more data is still needed to differentiate between closely looking food.
 
 ---
+
 ## 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).
 
 * **Hardware Type:** Tesla T4
-
 * **Hours used:** 1 hour estimate(max)
-
 * **Cloud Provider:** Google Cloud
-
 * **Compute Region:** us-central
-
 * **Carbon Emitted:** 80 grams of CO2eq (estimated)
 
 ---
+
 ## Technical Specifications
 
 ### Model Architecture and Objective
@@ -115,18 +116,14 @@ The model was trained using a Tesla T4 GPU on Google Cloud in the us-central reg
 ### Software
 
 * TensorFlow
-
 * TensorFlow Datasets
-
 * NumPy
-
 * Matplotlib
-
 * Scikit-learn
-
 * Helper functions from `helper_functions.py` (likely for plotting, data handling)
 
---
+---
+
 ## 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.
@@ -138,6 +135,7 @@ pip install tensorflow
 ```
 
 Then, you can load the model and make a prediction:
+
 ```python
 import tensorflow as tf
 import matplotlib.pyplot as plt