Noopy
/

train_model_yonsei

@@ -3,7 +3,7 @@ license: apache-2.0
 tags:
 - generated_from_trainer
 datasets:
-- cifar10
 metrics:
 - accuracy
 model-index:
@@ -13,15 +13,15 @@ model-index:
       name: Image Classification
       type: image-classification
     dataset:
-      name: cifar10
-      type: cifar10
-      config: plain_text
-      split: train[:5000]
-      args: plain_text
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.912
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
 # train_model_yonsei
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3947
-- Accuracy: 0.912
 ## Model description
@@ -66,21 +66,21 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.8929        | 0.99  | 62   | 1.7490          | 0.773    |
-| 0.9345        | 2.0   | 125  | 0.8608          | 0.864    |
-| 0.655         | 2.99  | 187  | 0.6876          | 0.866    |
-| 0.5273        | 4.0   | 250  | 0.5422          | 0.904    |
-| 0.4484        | 4.99  | 312  | 0.4926          | 0.893    |
-| 0.4012        | 6.0   | 375  | 0.4887          | 0.892    |
-| 0.353         | 6.99  | 437  | 0.4679          | 0.886    |
-| 0.3711        | 8.0   | 500  | 0.4204          | 0.899    |
-| 0.3079        | 8.99  | 562  | 0.4464          | 0.898    |
-| 0.2955        | 9.92  | 620  | 0.3947          | 0.912    |
 ### Framework versions
 - Transformers 4.30.2
 - Pytorch 2.0.1+cu118
-- Datasets 2.13.0
 - Tokenizers 0.13.3

 tags:
 - generated_from_trainer
 datasets:
+- imagefolder
 metrics:
 - accuracy
 model-index:
       name: Image Classification
       type: image-classification
     dataset:
+      name: imagefolder
+      type: imagefolder
+      config: dataset
+      split: test
+      args: dataset
     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.87
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 # train_model_yonsei
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.5148
+- Accuracy: 0.87
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.5711        | 0.98  | 11   | 1.4796          | 0.69     |
+| 1.3855        | 1.96  | 22   | 1.2302          | 0.74     |
+| 1.1544        | 2.93  | 33   | 1.0229          | 0.77     |
+| 0.9292        | 4.0   | 45   | 0.8371          | 0.8      |
+| 0.7715        | 4.98  | 56   | 0.7186          | 0.84     |
+| 0.6521        | 5.96  | 67   | 0.6353          | 0.85     |
+| 0.5736        | 6.93  | 78   | 0.5895          | 0.86     |
+| 0.4745        | 8.0   | 90   | 0.5891          | 0.85     |
+| 0.4361        | 8.98  | 101  | 0.5370          | 0.87     |
+| 0.4431        | 9.78  | 110  | 0.5148          | 0.87     |
 ### Framework versions
 - Transformers 4.30.2
 - Pytorch 2.0.1+cu118
+- Datasets 2.13.1
 - Tokenizers 0.13.3