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vit-base-patch16-224-ve-U13b-R

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- ---
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- license: apache-2.0
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- base_model: google/vit-base-patch16-224
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- tags:
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- - generated_from_trainer
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- datasets:
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- - imagefolder
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- metrics:
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- - accuracy
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- model-index:
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- - name: vit-base-patch16-224-ve-U13b-R
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- results:
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- - task:
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- name: Image Classification
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- type: image-classification
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- dataset:
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- name: imagefolder
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- type: imagefolder
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- config: default
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- split: validation
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- args: default
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- metrics:
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- - name: Accuracy
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- type: accuracy
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- value: 0.9347826086956522
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- ---
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-
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # vit-base-patch16-224-ve-U13b-R
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-
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- 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.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.3534
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- - Accuracy: 0.9348
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 5.5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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- - seed: 42
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- - gradient_accumulation_steps: 2
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- - total_train_batch_size: 16
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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- - lr_scheduler_warmup_ratio: 0.05
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- - num_epochs: 40
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.3157 | 0.99 | 51 | 1.2967 | 0.3478 |
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- | 0.9801 | 2.0 | 103 | 0.9966 | 0.5870 |
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- | 0.7385 | 2.99 | 154 | 0.7600 | 0.7174 |
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- | 0.572 | 4.0 | 206 | 0.6425 | 0.7826 |
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- | 0.3646 | 4.99 | 257 | 0.7687 | 0.6957 |
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- | 0.3033 | 6.0 | 309 | 0.6336 | 0.7391 |
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- | 0.3073 | 6.99 | 360 | 0.3534 | 0.9348 |
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- | 0.1623 | 8.0 | 412 | 0.8559 | 0.6739 |
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- | 0.1079 | 8.99 | 463 | 0.9730 | 0.7391 |
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- | 0.2703 | 10.0 | 515 | 0.7768 | 0.8043 |
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- | 0.178 | 10.99 | 566 | 0.8520 | 0.7826 |
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- | 0.2191 | 12.0 | 618 | 1.0049 | 0.7391 |
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- | 0.0597 | 12.99 | 669 | 0.8334 | 0.7609 |
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- | 0.0881 | 14.0 | 721 | 0.9985 | 0.7609 |
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- | 0.1265 | 14.99 | 772 | 0.9443 | 0.8043 |
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- | 0.0696 | 16.0 | 824 | 0.9878 | 0.8261 |
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- | 0.1198 | 16.99 | 875 | 0.8784 | 0.8043 |
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- | 0.1484 | 18.0 | 927 | 0.9595 | 0.7609 |
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- | 0.2887 | 18.99 | 978 | 1.0563 | 0.8043 |
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- | 0.1423 | 20.0 | 1030 | 0.8550 | 0.8043 |
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- | 0.083 | 20.99 | 1081 | 0.9093 | 0.7826 |
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- | 0.0695 | 22.0 | 1133 | 1.2758 | 0.6739 |
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- | 0.0285 | 22.99 | 1184 | 1.0852 | 0.7609 |
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- | 0.0132 | 24.0 | 1236 | 1.3341 | 0.6957 |
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- | 0.0957 | 24.99 | 1287 | 1.1965 | 0.7391 |
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- | 0.0633 | 26.0 | 1339 | 1.1199 | 0.7609 |
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- | 0.0705 | 26.99 | 1390 | 1.0551 | 0.8043 |
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- | 0.0564 | 28.0 | 1442 | 1.4332 | 0.7391 |
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- | 0.0798 | 28.99 | 1493 | 1.3855 | 0.7391 |
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- | 0.0326 | 30.0 | 1545 | 1.0534 | 0.8043 |
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- | 0.092 | 30.99 | 1596 | 1.1745 | 0.7609 |
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- | 0.1243 | 32.0 | 1648 | 1.1341 | 0.8043 |
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- | 0.062 | 32.99 | 1699 | 1.2648 | 0.7826 |
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- | 0.0941 | 34.0 | 1751 | 1.1236 | 0.7826 |
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- | 0.0119 | 34.99 | 1802 | 1.1303 | 0.8043 |
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- | 0.044 | 36.0 | 1854 | 1.1848 | 0.7826 |
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- | 0.0073 | 36.99 | 1905 | 1.1796 | 0.7609 |
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- | 0.0149 | 38.0 | 1957 | 1.2491 | 0.7826 |
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- | 0.0194 | 38.99 | 2008 | 1.1812 | 0.7826 |
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- | 0.0577 | 39.61 | 2040 | 1.1777 | 0.7609 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.36.2
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- - Pytorch 2.1.2+cu118
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- - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: google/vit-base-patch16-224
4
+ tags:
5
+ - generated_from_trainer
6
+ datasets:
7
+ - imagefolder
8
+ metrics:
9
+ - accuracy
10
+ model-index:
11
+ - name: vit-base-patch16-224-ve-U13b-R
12
+ results:
13
+ - task:
14
+ name: Image Classification
15
+ type: image-classification
16
+ dataset:
17
+ name: imagefolder
18
+ type: imagefolder
19
+ config: default
20
+ split: validation
21
+ args: default
22
+ metrics:
23
+ - name: Accuracy
24
+ type: accuracy
25
+ value: 0.9347826086956522
26
+ ---
27
+
28
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
29
+ should probably proofread and complete it, then remove this comment. -->
30
+
31
+ # vit-base-patch16-224-ve-U13b-R
32
+
33
+ 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.
34
+ It achieves the following results on the evaluation set:
35
+ - Loss: 0.3534
36
+ - Accuracy: 0.9348
37
+
38
+ ## Model description
39
+
40
+ More information needed
41
+
42
+ ## Intended uses & limitations
43
+
44
+ More information needed
45
+
46
+ ## Training and evaluation data
47
+
48
+ More information needed
49
+
50
+ ## Training procedure
51
+
52
+ ### Training hyperparameters
53
+
54
+
55
+
56
+ ### Training results
57
+
58
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
59
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
60
+ | 1.3157 | 0.99 | 51 | 1.2967 | 0.3478 |
61
+ | 0.9801 | 2.0 | 103 | 0.9966 | 0.5870 |
62
+ | 0.7385 | 2.99 | 154 | 0.7600 | 0.7174 |
63
+ | 0.572 | 4.0 | 206 | 0.6425 | 0.7826 |
64
+ | 0.3646 | 4.99 | 257 | 0.7687 | 0.6957 |
65
+ | 0.3033 | 6.0 | 309 | 0.6336 | 0.7391 |
66
+ | 0.3073 | 6.99 | 360 | 0.3534 | 0.9348 |
67
+ | 0.1623 | 8.0 | 412 | 0.8559 | 0.6739 |
68
+ | 0.1079 | 8.99 | 463 | 0.9730 | 0.7391 |
69
+ | 0.2703 | 10.0 | 515 | 0.7768 | 0.8043 |
70
+ | 0.178 | 10.99 | 566 | 0.8520 | 0.7826 |
71
+ | 0.2191 | 12.0 | 618 | 1.0049 | 0.7391 |
72
+ | 0.0597 | 12.99 | 669 | 0.8334 | 0.7609 |
73
+ | 0.0881 | 14.0 | 721 | 0.9985 | 0.7609 |
74
+ | 0.1265 | 14.99 | 772 | 0.9443 | 0.8043 |
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+ | 0.0696 | 16.0 | 824 | 0.9878 | 0.8261 |
76
+ | 0.1198 | 16.99 | 875 | 0.8784 | 0.8043 |
77
+ | 0.1484 | 18.0 | 927 | 0.9595 | 0.7609 |
78
+ | 0.2887 | 18.99 | 978 | 1.0563 | 0.8043 |
79
+ | 0.1423 | 20.0 | 1030 | 0.8550 | 0.8043 |
80
+ | 0.083 | 20.99 | 1081 | 0.9093 | 0.7826 |
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+ | 0.0695 | 22.0 | 1133 | 1.2758 | 0.6739 |
82
+ | 0.0285 | 22.99 | 1184 | 1.0852 | 0.7609 |
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+ | 0.0132 | 24.0 | 1236 | 1.3341 | 0.6957 |
84
+ | 0.0957 | 24.99 | 1287 | 1.1965 | 0.7391 |
85
+ | 0.0633 | 26.0 | 1339 | 1.1199 | 0.7609 |
86
+ | 0.0705 | 26.99 | 1390 | 1.0551 | 0.8043 |
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+ | 0.0564 | 28.0 | 1442 | 1.4332 | 0.7391 |
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+ | 0.0798 | 28.99 | 1493 | 1.3855 | 0.7391 |
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+ | 0.0326 | 30.0 | 1545 | 1.0534 | 0.8043 |
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+ | 0.092 | 30.99 | 1596 | 1.1745 | 0.7609 |
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+ | 0.1243 | 32.0 | 1648 | 1.1341 | 0.8043 |
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+ | 0.062 | 32.99 | 1699 | 1.2648 | 0.7826 |
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+ | 0.0941 | 34.0 | 1751 | 1.1236 | 0.7826 |
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+ | 0.0119 | 34.99 | 1802 | 1.1303 | 0.8043 |
95
+ | 0.044 | 36.0 | 1854 | 1.1848 | 0.7826 |
96
+ | 0.0073 | 36.99 | 1905 | 1.1796 | 0.7609 |
97
+ | 0.0149 | 38.0 | 1957 | 1.2491 | 0.7826 |
98
+ | 0.0194 | 38.99 | 2008 | 1.1812 | 0.7826 |
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+ | 0.0577 | 39.61 | 2040 | 1.1777 | 0.7609 |
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+
101
+
102
+ ### Framework versions
103
+
104
+ - Transformers 4.36.2
105
+ - Pytorch 2.1.2+cu118
106
+ - Datasets 2.16.1
107
+ - Tokenizers 0.15.0