update model card README.md

README.md

@@ -1,3 +1,102 @@
 ---
-license: unknown
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+model-index:
+- name: convnext-tiny-224-finetuned-brs
+  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: 0.8235294117647058
+    - name: F1
+      type: f1
+      value: 0.7272727272727272
 ---
+
+<!-- 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. -->
+
+# convnext-tiny-224-finetuned-brs
+
+This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8667
+- Accuracy: 0.8235
+- F1: 0.7273
+- Precision (ppv): 0.8
+- Recall (sensitivity): 0.6667
+- Specificity: 0.9091
+- Npv: 0.8333
+- Auc: 0.7879
+
+## 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: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision (ppv) | Recall (sensitivity) | Specificity | Npv    | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
+| 0.6766        | 6.25  | 100  | 0.7002          | 0.4706   | 0.5263 | 0.3846          | 0.8333               | 0.2727      | 0.75   | 0.5530 |
+| 0.6408        | 12.49 | 200  | 0.6770          | 0.6471   | 0.5714 | 0.5             | 0.6667               | 0.6364      | 0.7778 | 0.6515 |
+| 0.464         | 18.74 | 300  | 0.6624          | 0.5882   | 0.5882 | 0.4545          | 0.8333               | 0.4545      | 0.8333 | 0.6439 |
+| 0.4295        | 24.98 | 400  | 0.6938          | 0.5294   | 0.5    | 0.4             | 0.6667               | 0.4545      | 0.7143 | 0.5606 |
+| 0.3952        | 31.25 | 500  | 0.5974          | 0.7059   | 0.6154 | 0.5714          | 0.6667               | 0.7273      | 0.8    | 0.6970 |
+| 0.1082        | 37.49 | 600  | 0.6163          | 0.6471   | 0.5    | 0.5             | 0.5                  | 0.7273      | 0.7273 | 0.6136 |
+| 0.1997        | 43.74 | 700  | 0.6155          | 0.7059   | 0.6154 | 0.5714          | 0.6667               | 0.7273      | 0.8    | 0.6970 |
+| 0.1267        | 49.98 | 800  | 0.9063          | 0.6471   | 0.5714 | 0.5             | 0.6667               | 0.6364      | 0.7778 | 0.6515 |
+| 0.1178        | 56.25 | 900  | 0.8672          | 0.7059   | 0.6667 | 0.5556          | 0.8333               | 0.6364      | 0.875  | 0.7348 |
+| 0.2008        | 62.49 | 1000 | 0.7049          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
+| 0.0996        | 68.74 | 1100 | 0.4510          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
+| 0.0115        | 74.98 | 1200 | 0.7561          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
+| 0.0177        | 81.25 | 1300 | 1.0400          | 0.7059   | 0.6667 | 0.5556          | 0.8333               | 0.6364      | 0.875  | 0.7348 |
+| 0.0261        | 87.49 | 1400 | 0.9139          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
+| 0.028         | 93.74 | 1500 | 0.7367          | 0.7647   | 0.7143 | 0.625           | 0.8333               | 0.7273      | 0.8889 | 0.7803 |
+| 0.0056        | 99.98 | 1600 | 0.8667          | 0.8235   | 0.7273 | 0.8             | 0.6667               | 0.9091      | 0.8333 | 0.7879 |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1