Model save

README.md

@@ -7,6 +7,8 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- precision
+- recall
 model-index:
 - name: beit-base-patch16-224
   results:
@@ -22,7 +24,13 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6766666666666666
+      value: 0.7933333333333333
+    - name: Precision
+      type: precision
+      value: 0.7853286177424108
+    - name: Recall
+      type: recall
+      value: 0.7933333333333333
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6115
-- Accuracy: 0.6767
+- Loss: 0.8531
+- Accuracy: 0.7933
+- Precision: 0.7853
+- Recall: 0.7933
+- F1 Score: 0.7662
 
 ## Model description
 
@@ -53,28 +64,40 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 32
+- train_batch_size: 64
+- eval_batch_size: 64
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 128
+- total_train_batch_size: 256
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
+- num_epochs: 15
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 8    | 0.8224          | 0.7333   |
-| 4.1576        | 2.0   | 16   | 0.5882          | 0.7417   |
-| 0.66          | 3.0   | 24   | 0.5830          | 0.7667   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| No log        | 1.0   | 4    | 0.5815          | 0.7292   | 0.6273    | 0.7292 | 0.6259   |
+| No log        | 2.0   | 8    | 0.5493          | 0.7333   | 0.6901    | 0.7333 | 0.6863   |
+| No log        | 3.0   | 12   | 0.5545          | 0.7667   | 0.7575    | 0.7667 | 0.7147   |
+| 0.5698        | 4.0   | 16   | 0.5706          | 0.7667   | 0.7503    | 0.7667 | 0.7221   |
+| 0.5698        | 5.0   | 20   | 0.5800          | 0.7667   | 0.7575    | 0.7667 | 0.7147   |
+| 0.5698        | 6.0   | 24   | 0.5929          | 0.7833   | 0.7772    | 0.7833 | 0.7451   |
+| 0.5698        | 7.0   | 28   | 0.5783          | 0.7833   | 0.7677    | 0.7833 | 0.7672   |
+| 0.2938        | 8.0   | 32   | 0.5665          | 0.7875   | 0.7793    | 0.7875 | 0.7821   |
+| 0.2938        | 9.0   | 36   | 0.7751          | 0.7875   | 0.7770    | 0.7875 | 0.7571   |
+| 0.2938        | 10.0  | 40   | 0.7088          | 0.7917   | 0.7816    | 0.7917 | 0.7843   |
+| 0.2938        | 11.0  | 44   | 0.8799          | 0.8042   | 0.7972    | 0.8042 | 0.7808   |
+| 0.0834        | 12.0  | 48   | 0.8367          | 0.7875   | 0.7793    | 0.7875 | 0.7821   |
+| 0.0834        | 13.0  | 52   | 0.9200          | 0.7958   | 0.7834    | 0.7958 | 0.7758   |
+| 0.0834        | 14.0  | 56   | 0.8821          | 0.8      | 0.7879    | 0.8    | 0.7869   |
+| 0.0358        | 15.0  | 60   | 0.8674          | 0.7875   | 0.7753    | 0.7875 | 0.7777   |
 
 
 ### Framework versions
 
-- Transformers 4.32.1
+- Transformers 4.33.2
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.4
+- Datasets 2.14.5
 - Tokenizers 0.13.3

pytorch_model.bin

@@ -1,3 +1,3 @@
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+oid sha256:8fbdd82dc506c05384601626586b6b8d6934ca1cc1490b04737053aa031be82b
 size 343130249