Hugging Face's logo

AykeeSalazar
/
vc-bantai-vit-withoutAMBI-adunest-v3

Image Classification
Transformers
PyTorch
TensorBoard
vit
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
AykeeSalazar commited on
Commit
0b22199
•
1 Parent(s): 148aab4

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +112 -0
README.md ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - imagefolder
7
+ metrics:
8
+ - accuracy
9
+ model-index:
10
+ - name: vc-bantai-vit-withoutAMBI-adunest-v3
11
+ results:
12
+ - task:
13
+ name: Image Classification
14
+ type: image-classification
15
+ dataset:
16
+ name: imagefolder
17
+ type: imagefolder
18
+ args: Violation-Classification---Raw-10
19
+ metrics:
20
+ - name: Accuracy
21
+ type: accuracy
22
+ value: 0.8218352310783658
23
+ ---
24
+
25
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
26
+ should probably proofread and complete it, then remove this comment. -->
27
+
28
+ # vc-bantai-vit-withoutAMBI-adunest-v3
29
+
30
+ 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.
31
+ It achieves the following results on the evaluation set:
32
+ - Loss: 0.8889
33
+ - Accuracy: 0.8218
34
+
35
+ ## Model description
36
+
37
+ More information needed
38
+
39
+ ## Intended uses & limitations
40
+
41
+ More information needed
42
+
43
+ ## Training and evaluation data
44
+
45
+ More information needed
46
+
47
+ ## Training procedure
48
+
49
+ ### Training hyperparameters
50
+
51
+ The following hyperparameters were used during training:
52
+ - learning_rate: 0.0005
53
+ - train_batch_size: 32
54
+ - eval_batch_size: 32
55
+ - seed: 42
56
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
57
+ - lr_scheduler_type: linear
58
+ - num_epochs: 200
59
+ - mixed_precision_training: Native AMP
60
+
61
+ ### Training results
62
+
63
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
64
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
65
+ | No log | 0.38 | 100 | 0.8208 | 0.7147 |
66
+ | No log | 0.76 | 200 | 0.8861 | 0.7595 |
67
+ | No log | 1.14 | 300 | 0.4306 | 0.7910 |
68
+ | No log | 1.52 | 400 | 0.5222 | 0.8245 |
69
+ | 0.3448 | 1.9 | 500 | 0.8621 | 0.7602 |
70
+ | 0.3448 | 2.28 | 600 | 0.2902 | 0.8801 |
71
+ | 0.3448 | 2.66 | 700 | 0.3687 | 0.8426 |
72
+ | 0.3448 | 3.04 | 800 | 0.3585 | 0.8694 |
73
+ | 0.3448 | 3.42 | 900 | 0.6546 | 0.7897 |
74
+ | 0.2183 | 3.8 | 1000 | 0.3881 | 0.8272 |
75
+ | 0.2183 | 4.18 | 1100 | 0.9650 | 0.7709 |
76
+ | 0.2183 | 4.56 | 1200 | 0.6444 | 0.7917 |
77
+ | 0.2183 | 4.94 | 1300 | 0.4685 | 0.8707 |
78
+ | 0.2183 | 5.32 | 1400 | 0.4972 | 0.8506 |
79
+ | 0.157 | 5.7 | 1500 | 0.4010 | 0.8513 |
80
+ | 0.157 | 6.08 | 1600 | 0.4629 | 0.8419 |
81
+ | 0.157 | 6.46 | 1700 | 0.4258 | 0.8714 |
82
+ | 0.157 | 6.84 | 1800 | 0.4383 | 0.8573 |
83
+ | 0.157 | 7.22 | 1900 | 0.5324 | 0.8493 |
84
+ | 0.113 | 7.6 | 2000 | 0.3212 | 0.8942 |
85
+ | 0.113 | 7.98 | 2100 | 0.8621 | 0.8326 |
86
+ | 0.113 | 8.37 | 2200 | 0.6050 | 0.8131 |
87
+ | 0.113 | 8.75 | 2300 | 0.7173 | 0.7991 |
88
+ | 0.113 | 9.13 | 2400 | 0.5313 | 0.8125 |
89
+ | 0.0921 | 9.51 | 2500 | 0.6584 | 0.8158 |
90
+ | 0.0921 | 9.89 | 2600 | 0.8727 | 0.7930 |
91
+ | 0.0921 | 10.27 | 2700 | 0.4222 | 0.8922 |
92
+ | 0.0921 | 10.65 | 2800 | 0.5811 | 0.8265 |
93
+ | 0.0921 | 11.03 | 2900 | 0.6175 | 0.8372 |
94
+ | 0.0701 | 11.41 | 3000 | 0.3914 | 0.8835 |
95
+ | 0.0701 | 11.79 | 3100 | 0.3364 | 0.8654 |
96
+ | 0.0701 | 12.17 | 3200 | 0.6223 | 0.8359 |
97
+ | 0.0701 | 12.55 | 3300 | 0.7830 | 0.8125 |
98
+ | 0.0701 | 12.93 | 3400 | 0.4356 | 0.8942 |
99
+ | 0.0552 | 13.31 | 3500 | 0.7553 | 0.8232 |
100
+ | 0.0552 | 13.69 | 3600 | 0.9107 | 0.8292 |
101
+ | 0.0552 | 14.07 | 3700 | 0.6108 | 0.8580 |
102
+ | 0.0552 | 14.45 | 3800 | 0.5732 | 0.8567 |
103
+ | 0.0552 | 14.83 | 3900 | 0.5087 | 0.8614 |
104
+ | 0.0482 | 15.21 | 4000 | 0.8889 | 0.8218 |
105
+
106
+
107
+ ### Framework versions
108
+
109
+ - Transformers 4.20.1
110
+ - Pytorch 1.12.0+cu113
111
+ - Datasets 2.3.2
112
+ - Tokenizers 0.12.1