Hugging Face's logo

minhdang1
/
vit-base-patch16-224-finetuned-context-classifier

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
minhdang1 commited on
Commit
3b97c15
•
1 Parent(s): 76003df

Model save

Browse files
Files changed (1) hide show
  1. README.md +127 -0
README.md ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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-finetuned-context-classifier
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: test
21
+ args: default
22
+ metrics:
23
+ - name: Accuracy
24
+ type: accuracy
25
+ value: 0.8187702265372169
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-finetuned-context-classifier
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: 0.7157
36
+ - Accuracy: 0.8188
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
+ The following hyperparameters were used during training:
55
+ - learning_rate: 5e-05
56
+ - train_batch_size: 128
57
+ - eval_batch_size: 128
58
+ - seed: 42
59
+ - gradient_accumulation_steps: 4
60
+ - total_train_batch_size: 512
61
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
+ - lr_scheduler_type: linear
63
+ - lr_scheduler_warmup_ratio: 0.1
64
+ - num_epochs: 100
65
+
66
+ ### Training results
67
+
68
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
69
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
70
+ | 1.3586 | 2.0 | 10 | 1.2322 | 0.3916 |
71
+ | 1.0841 | 4.0 | 20 | 0.8444 | 0.6958 |
72
+ | 0.7282 | 6.0 | 30 | 0.5498 | 0.7767 |
73
+ | 0.4768 | 8.0 | 40 | 0.4273 | 0.8155 |
74
+ | 0.3332 | 10.0 | 50 | 0.4059 | 0.8220 |
75
+ | 0.242 | 12.0 | 60 | 0.4272 | 0.8252 |
76
+ | 0.1737 | 14.0 | 70 | 0.4372 | 0.8188 |
77
+ | 0.1266 | 16.0 | 80 | 0.4495 | 0.8123 |
78
+ | 0.1089 | 18.0 | 90 | 0.4877 | 0.8091 |
79
+ | 0.0837 | 20.0 | 100 | 0.5318 | 0.8058 |
80
+ | 0.0687 | 22.0 | 110 | 0.5300 | 0.7961 |
81
+ | 0.0667 | 24.0 | 120 | 0.6253 | 0.7994 |
82
+ | 0.0581 | 26.0 | 130 | 0.5495 | 0.8220 |
83
+ | 0.0574 | 28.0 | 140 | 0.5646 | 0.8188 |
84
+ | 0.0564 | 30.0 | 150 | 0.5990 | 0.8252 |
85
+ | 0.0492 | 32.0 | 160 | 0.6436 | 0.8155 |
86
+ | 0.0406 | 34.0 | 170 | 0.6225 | 0.8091 |
87
+ | 0.0411 | 36.0 | 180 | 0.6168 | 0.8123 |
88
+ | 0.0381 | 38.0 | 190 | 0.6731 | 0.8123 |
89
+ | 0.0358 | 40.0 | 200 | 0.6198 | 0.7961 |
90
+ | 0.0354 | 42.0 | 210 | 0.6216 | 0.8091 |
91
+ | 0.0358 | 44.0 | 220 | 0.6933 | 0.8091 |
92
+ | 0.037 | 46.0 | 230 | 0.6488 | 0.8188 |
93
+ | 0.0344 | 48.0 | 240 | 0.6546 | 0.8220 |
94
+ | 0.0335 | 50.0 | 250 | 0.6399 | 0.7994 |
95
+ | 0.0297 | 52.0 | 260 | 0.6553 | 0.8123 |
96
+ | 0.0318 | 54.0 | 270 | 0.6996 | 0.7896 |
97
+ | 0.0254 | 56.0 | 280 | 0.6809 | 0.7961 |
98
+ | 0.0322 | 58.0 | 290 | 0.7048 | 0.7896 |
99
+ | 0.024 | 60.0 | 300 | 0.6869 | 0.8123 |
100
+ | 0.0255 | 62.0 | 310 | 0.7099 | 0.8058 |
101
+ | 0.0266 | 64.0 | 320 | 0.6894 | 0.8091 |
102
+ | 0.0243 | 66.0 | 330 | 0.7604 | 0.8091 |
103
+ | 0.0232 | 68.0 | 340 | 0.6983 | 0.8123 |
104
+ | 0.019 | 70.0 | 350 | 0.6834 | 0.8091 |
105
+ | 0.0235 | 72.0 | 360 | 0.7102 | 0.8091 |
106
+ | 0.0262 | 74.0 | 370 | 0.6902 | 0.8155 |
107
+ | 0.0206 | 76.0 | 380 | 0.6662 | 0.8091 |
108
+ | 0.0238 | 78.0 | 390 | 0.7109 | 0.8220 |
109
+ | 0.0202 | 80.0 | 400 | 0.7061 | 0.8058 |
110
+ | 0.0204 | 82.0 | 410 | 0.7291 | 0.8155 |
111
+ | 0.0231 | 84.0 | 420 | 0.7103 | 0.8091 |
112
+ | 0.0217 | 86.0 | 430 | 0.7050 | 0.8123 |
113
+ | 0.021 | 88.0 | 440 | 0.7037 | 0.8155 |
114
+ | 0.0207 | 90.0 | 450 | 0.6996 | 0.8058 |
115
+ | 0.0163 | 92.0 | 460 | 0.7137 | 0.8091 |
116
+ | 0.0181 | 94.0 | 470 | 0.7153 | 0.8155 |
117
+ | 0.0225 | 96.0 | 480 | 0.7105 | 0.8123 |
118
+ | 0.0185 | 98.0 | 490 | 0.7140 | 0.8155 |
119
+ | 0.0219 | 100.0 | 500 | 0.7157 | 0.8188 |
120
+
121
+
122
+ ### Framework versions
123
+
124
+ - Transformers 4.35.0
125
+ - Pytorch 2.1.0+cu121
126
+ - Datasets 2.14.4
127
+ - Tokenizers 0.14.1