Update README.md

README.md

@@ -3,31 +3,34 @@ license: apache-2.0
 base_model: hustvl/yolos-small
 tags:
 - generated_from_trainer
+- Blood Cells
+- biology
+- medical
 datasets:
 - blood-cell-object-detection
 model-index:
 - name: yolos-small-Blood_Cell_Object_Detection
   results: []
+language:
+- en
+pipeline_tag: object-detection
 ---
 
-<!-- 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. -->
-
 # yolos-small-Blood_Cell_Object_Detection
 
 This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the blood-cell-object-detection dataset.
 
 ## Model description
 
-More information needed
+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/Object%20Detection/Blood%20Cell%20Object%20Detection/Blood_Cell_Object_Detection_YOLOS.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://huggingface.co/datasets/keremberke/blood-cell-object-detection
 
 ## Training procedure
 
@@ -44,11 +47,24 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+| Metric Name | IoU | Area | maxDets | Metric Value |
+|:-----:|:-----:|:-----:|:-----:|:-----:|
+| Average Precision (AP) | IoU=0.50:0.95 | all | maxDets=100 | 0.344 |
+| Average Precision (AP) | IoU=0.50 | all | maxDets=100 | 0.579 |
+| Average Precision (AP) | IoU=0.75 | all | maxDets=100 | 0.374 |
+| Average Precision (AP) | IoU=0.50:0.95 | small | maxDets=100 | 0.097 |
+| Average Precision (AP) | IoU=0.50:0.95 | medium | maxDets=100 | 0.258 |
+| Average Precision (AP) | IoU=0.50:0.95 | large | maxDets=100 | 0.224 |
+| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=1 | 0.210 |
+| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=10 | 0.376 |
+| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=100 | 0.448 |
+| Average Recall (AR) | IoU=0.50:0.95 | small | maxDets=100 | 0.108 |
+| Average Recall (AR) | IoU=0.50:0.95 | medium | maxDets=100 | 0.375 |
+| Average Recall (AR) | IoU=0.50:0.95 | large | maxDets=100 | 0.448 |
 
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.3
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3