Update README.md

add testing data confusion matrix

README.md

@@ -1,81 +1,86 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: google/vit-base-patch16-224
-tags:
-- generated_from_trainer
-datasets:
-- imagefolder
-metrics:
-- accuracy
-model-index:
-- name: vit-base-patch16-224-finetuned-ISIC-dec2024
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: imagefolder
-      type: imagefolder
-      config: default
-      split: train
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.9380236925744004
----
-
-<!-- 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-finetuned-ISIC-dec2024
-
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1523
-- Accuracy: 0.9380
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| 0.8152        | 0.9985 | 486  | 0.1791          | 0.9223   |
-| 0.6467        | 1.9985 | 972  | 0.1590          | 0.9361   |
-| 0.5399        | 2.9985 | 1458 | 0.1523          | 0.9380   |
-
-
-### Framework versions
-
-- Transformers 4.47.1
-- Pytorch 2.6.0.dev20241225+cu126
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-finetuned-ISIC-dec2024
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9380236925744004
+---
+
+<!-- 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-finetuned-ISIC-dec2024
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1523
+- Accuracy: 0.9380
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.8152        | 0.9985 | 486  | 0.1791          | 0.9223   |
+| 0.6467        | 1.9985 | 972  | 0.1590          | 0.9361   |
+| 0.5399        | 2.9985 | 1458 | 0.1523          | 0.9380   |
+
+Testing data confusion values:
+True positive: 1301
+False positive: 301
+True negative: 14912
+False negative: 792
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.6.0.dev20241225+cu126
+- Datasets 3.2.0
+- Tokenizers 0.21.0