Update README.md

README.md

@@ -6,6 +6,9 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: vit-base-patch16-224-in21k_GI_diagnosis
   results:
@@ -22,14 +25,14 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.9375
+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. -->
-
 # vit-base-patch16-224-in21k_GI_diagnosis
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
 It achieves the following results on the evaluation set:
 - Loss: 0.2538
 - Accuracy: 0.9375
@@ -45,15 +48,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a multiclass image classification model of GI diagnosis'.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Diagnoses%20from%20Colonoscopy%20Images/diagnosis_from_colonoscopy_image_ViT.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/francismon/curated-colon-dataset-for-deep-learning
 
 ## Training procedure
 
@@ -82,4 +87,4 @@ The following hyperparameters were used during training:
 - Transformers 4.22.2
 - Pytorch 1.12.1
 - Datasets 2.5.2
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1