Model save

README.md

@@ -7,6 +7,8 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- precision
+- recall
 model-index:
 - name: vit-base-patch16-224
   results:
@@ -22,7 +24,13 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7433333333333333
+      value: 0.8033333333333333
+    - name: Precision
+      type: precision
+      value: 0.7988653846153846
+    - name: Recall
+      type: recall
+      value: 0.8033333333333333
 ---
 
 <!-- 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 [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: 0.6275
-- Accuracy: 0.7433
+- Loss: 0.4775
+- Accuracy: 0.8033
+- Precision: 0.7989
+- Recall: 0.8033
+- F1 Score: 0.7784
 
 ## Model description
 
@@ -61,20 +72,27 @@ The following hyperparameters were used during training:
 - 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: 10
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 8    | 2.6943          | 0.6833   |
-| 5.3331        | 2.0   | 16   | 0.6308          | 0.7417   |
-| 0.6715        | 3.0   | 24   | 0.6677          | 0.6458   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| No log        | 1.0   | 8    | 0.5941          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
+| 0.6385        | 2.0   | 16   | 0.5391          | 0.775    | 0.7830    | 0.775  | 0.7210   |
+| 0.546         | 3.0   | 24   | 0.5417          | 0.775    | 0.7658    | 0.775  | 0.7321   |
+| 0.481         | 4.0   | 32   | 0.5486          | 0.7833   | 0.8030    | 0.7833 | 0.7313   |
+| 0.3841        | 5.0   | 40   | 0.5420          | 0.7875   | 0.7825    | 0.7875 | 0.7515   |
+| 0.3841        | 6.0   | 48   | 0.5246          | 0.8292   | 0.8358    | 0.8292 | 0.8068   |
+| 0.2565        | 7.0   | 56   | 0.5763          | 0.8083   | 0.8070    | 0.8083 | 0.7821   |
+| 0.1605        | 8.0   | 64   | 0.5433          | 0.825    | 0.8180    | 0.825  | 0.8120   |
+| 0.0824        | 9.0   | 72   | 0.6010          | 0.8125   | 0.8027    | 0.8125 | 0.7994   |
+| 0.0489        | 10.0  | 80   | 0.6063          | 0.8125   | 0.8032    | 0.8125 | 0.7977   |
 
 
 ### 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:a10521d07dc61f533d0d5a9bec8d2c44bba11613456d4bab737402f7e4b9eda4
 size 343268717