Update README.md

README.md

@@ -6,6 +6,9 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: efficientformer-l3-300-Brain_Tumors_Image_Classification
   results:
@@ -22,11 +25,11 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.7817258883248731
+language:
+- en
+pipeline_tag: image-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # efficientformer-l3-300-Brain_Tumors_Image_Classification
 
 This model is a fine-tuned version of [snap-research/efficientformer-l3-300](https://huggingface.co/snap-research/efficientformer-l3-300) on the imagefolder dataset.
@@ -43,17 +46,42 @@ It achieves the following results on the evaluation set:
 - Micro precision: 0.7817
 - Macro precision: 0.8613
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+<div style="text-align: center;">
+<h2>
+    Model Description
+</h2>
+<a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/EfficientFormer-%20Image%20Classification.ipynb">
+    Click here for the code that I used to create this model
+</a>
+This project is part of a comparison of seventeen (17) transformers. 
+<a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/README.md">
+    Click here to see the README markdown file for the full project
+</a>
+<h2>
+    Intended Uses & Limitations
+</h2>
+This model is intended to demonstrate my ability to solve a complex problem using technology.
+
+
+<h2>
+    Training & Evaluation Data
+</h2>
+<a href="https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri">
+    Brain Tumor Image Classification Dataset
+</a>
+<h2>
+    Sample Images
+</h2>
+<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Sample%20Images.png" />
+<h2>
+    Class Distribution of Training Dataset
+</h2>
+<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Training%20Dataset.png"/>
+<h2>
+    Class Distribution of Evaluation Dataset
+</h2>
+<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Testing%20Dataset.png"/>
+</div>
 
 ## Training procedure
 
@@ -76,10 +104,9 @@ The following hyperparameters were used during training:
 | 1.2856        | 2.0   | 360  | 2.1421          | 0.7563   | 0.7146      | 0.7563   | 0.7211   | 0.7563          | 0.7563       | 0.7471       | 0.8381             | 0.7563          | 0.8551          |
 | 0.1405        | 3.0   | 540  | 2.2761          | 0.7817   | 0.7381      | 0.7817   | 0.7465   | 0.7817          | 0.7817       | 0.7771       | 0.8442             | 0.7817          | 0.8613          |
 
-
 ### Framework versions
 
 - Transformers 4.28.1
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3