Update README.md

README.md

@@ -8,11 +8,17 @@ tags:
 model-index:
 - name: mit-b0-CMP_semantic_seg_with_mps_v2
   results: []
+datasets:
+- Xpitfire/cmp_facade
+metrics:
+- mean_iou
+pipeline_tag: image-segmentation
 ---
 
 # mit-b0-CMP_semantic_seg_with_mps_v2
 
-This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0).
+
 It achieves the following results on the evaluation set:
 - Loss: 1.0863
 - Mean Iou: 0.4097
@@ -47,15 +53,15 @@ It achieves the following results on the evaluation set:
 
 ## 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/Image%20Segmentation/Trained%2C%20But%20to%20My%20Standard/Center%20for%20Machine%20Perception/Version%202/Center%20for%20Machine%20Perception%20-%20semantic_segmentation_v2.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to use it, but remember that it is at your own risk/peril.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://huggingface.co/datasets/Xpitfire/cmp_facade
 
 ## Training procedure
 
@@ -100,7 +106,7 @@ The following hyperparameters were used during training:
 | 0.4601        | 24.0  | 4536 | 1.0040          | 0.4104   | 0.5551        | 0.6948           | 0.6061 | 0.5756 | 0.5721 | 0.3086 | 0.3771 | 0.3707 | 0.4459 | 0.4242 | 0.2665 | 0.4104 | 0.1942 | 0.3732 | 0.7277 | 0.7718 | 0.7095 | 0.4789 | 0.5401 | 0.5080 | 0.6040 | 0.5314 | 0.4573 | 0.5414 | 0.2853 | 0.5062 |
 | 0.4544        | 25.0  | 4725 | 1.0093          | 0.4093   | 0.5652        | 0.6899           | 0.5826 | 0.5745 | 0.5742 | 0.3109 | 0.3765 | 0.3784 | 0.4441 | 0.4184 | 0.2609 | 0.4219 | 0.1930 | 0.3765 | 0.6781 | 0.7703 | 0.7305 | 0.5102 | 0.5954 | 0.5311 | 0.5960 | 0.5286 | 0.4647 | 0.5861 | 0.2676 | 0.5242 |
 | 0.4421        | 26.0  | 4914 | 1.0434          | 0.4064   | 0.5448        | 0.6938           | 0.5783 | 0.5821 | 0.5770 | 0.2985 | 0.3885 | 0.3582 | 0.4458 | 0.4220 | 0.2717 | 0.4260 | 0.1690 | 0.3600 | 0.6603 | 0.7989 | 0.7349 | 0.4689 | 0.5677 | 0.4620 | 0.6111 | 0.5258 | 0.4556 | 0.5889 | 0.2110 | 0.4530 |
-| 0.4293        | 27.0  | 5103 | 1.0391          | 0.4076   | 0.5571        | 0.6908           | 0.5764 | 0.5777 | 0.5749 | 0.2868 | 0.3824 | 0.3857 | 0.4450 | 0.4170 | 0.2644 | 0.4295 | 0.1922
+| 0.4293        | 27.0  | 5103 | 1.0391          | 0.4076   | 0.5571        | 0.6908           | 0.5764 | 0.5777 | 0.5749 | 0.2868 | 0.3824 | 0.3857 | 0.4450 | 0.4170 | 0.2644 | 0.4295 | 0.1922 |
 | 0.4312        | 28.0  | 5292 | 1.0037          | 0.4100   | 0.5534        | 0.6958           | 0.6023 | 0.5776 | 0.5769 | 0.2964 | 0.3759 | 0.3758 | 0.4464 | 0.4245 | 0.2712 | 0.4083 | 0.1967 | 0.3680 | 0.7218 | 0.7735 | 0.7273 | 0.4297 | 0.6001 | 0.5321
 | 0.4309        | 29.0  | 5481 | 1.0288          | 0.4101   | 0.5493        | 0.6968           | 0.6043 | 0.5814 | 0.5728 | 0.2882 | 0.3867 | 0.3841 | 0.4369 | 0.4254 | 0.2659 | 0.4252 | 0.2106 | 0.3391 | 0.7054 | 0.7948 | 0.7009 | 0.4552 | 0.5413 | 0.5357 | 0.5421 | 0.5250 | 0.4701 | 0.5949 | 0.3048 | 0.4213 |
 | 0.4146        | 30.0  | 5670 | 1.0602          | 0.4062   | 0.5445        | 0.6928           | 0.5840 | 0.5792 | 0.5750 | 0.2859 | 0.3839 | 0.3786 | 0.4479 | 0.4259 | 0.2664 | 0.3947 | 0.1753 | 0.3780 | 0.6744 | 0.8004 | 0.7289 | 0.4421 | 0.5410 | 0.5409 | 0.5822 | 0.5334 | 0.4790 | 0.5028 | 0.2177 | 0.4910 |
@@ -132,4 +138,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.1
 - Pytorch 1.12.1
 - Datasets 2.9.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1