Hugging Face's logo

khleeloo
/
vit-base-skin

Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
khleeloo commited on
Commit
072f8cc
·
1 Parent(s): bdefbbf

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +43 -12
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
19
 
20
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
21
  It achieves the following results on the evaluation set:
22
- - Loss: 0.6813
23
- - Accuracy: 0.8497
24
- - F1: 0.8487
25
- - Precision: 0.8530
26
- - Recall: 0.8497
27
 
28
  ## Model description
29
 
@@ -44,7 +44,7 @@ More information needed
44
  The following hyperparameters were used during training:
45
  - learning_rate: 0.0002
46
  - train_batch_size: 16
47
- - eval_batch_size: 16
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
@@ -55,12 +55,43 @@ The following hyperparameters were used during training:
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
57
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
58
- | 0.4952 | 1.0 | 626 | 0.5520 | 0.8083 | 0.8152 | 0.8906 | 0.8083 |
59
- | 0.406 | 2.0 | 1252 | 0.4405 | 0.8808 | 0.8740 | 0.8807 | 0.8808 |
60
- | 0.3248 | 3.0 | 1878 | 0.4204 | 0.8446 | 0.8349 | 0.8381 | 0.8446 |
61
- | 0.1064 | 4.0 | 2504 | 0.6135 | 0.8446 | 0.8375 | 0.8336 | 0.8446 |
62
- | 0.0053 | 5.0 | 3130 | 0.5843 | 0.8705 | 0.8716 | 0.8762 | 0.8705 |
63
- | 0.0024 | 6.0 | 3756 | 0.6813 | 0.8497 | 0.8487 | 0.8530 | 0.8497 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64
 
65
 
66
  ### Framework versions
 
19
 
20
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
21
  It achieves the following results on the evaluation set:
22
+ - Loss: 0.7902
23
+ - Accuracy: 0.8446
24
+ - F1: 0.8443
25
+ - Precision: 0.8449
26
+ - Recall: 0.8446
27
 
28
  ## Model description
29
 
 
44
  The following hyperparameters were used during training:
45
  - learning_rate: 0.0002
46
  - train_batch_size: 16
47
+ - eval_batch_size: 8
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
 
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
57
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
58
+ | 0.8192 | 0.16 | 100 | 0.7593 | 0.7098 | 0.6664 | 0.7150 | 0.7098 |
59
+ | 0.7637 | 0.32 | 200 | 0.6606 | 0.7513 | 0.7008 | 0.7697 | 0.7513 |
60
+ | 0.6177 | 0.48 | 300 | 0.5816 | 0.7927 | 0.7706 | 0.7834 | 0.7927 |
61
+ | 0.837 | 0.64 | 400 | 0.5884 | 0.7927 | 0.7831 | 0.7980 | 0.7927 |
62
+ | 0.6409 | 0.8 | 500 | 0.5473 | 0.8290 | 0.8270 | 0.8419 | 0.8290 |
63
+ | 0.5896 | 0.96 | 600 | 0.5079 | 0.8083 | 0.7904 | 0.8440 | 0.8083 |
64
+ | 0.4101 | 1.12 | 700 | 0.4893 | 0.8290 | 0.8219 | 0.8343 | 0.8290 |
65
+ | 0.6753 | 1.28 | 800 | 0.6570 | 0.7668 | 0.7800 | 0.8298 | 0.7668 |
66
+ | 0.3907 | 1.44 | 900 | 0.4257 | 0.8238 | 0.8152 | 0.8117 | 0.8238 |
67
+ | 0.447 | 1.6 | 1000 | 0.5717 | 0.8031 | 0.8030 | 0.8447 | 0.8031 |
68
+ | 0.4131 | 1.76 | 1100 | 0.4189 | 0.8342 | 0.8271 | 0.8304 | 0.8342 |
69
+ | 0.3913 | 1.92 | 1200 | 0.3728 | 0.8860 | 0.8877 | 0.8995 | 0.8860 |
70
+ | 0.2686 | 2.08 | 1300 | 0.5161 | 0.7979 | 0.7995 | 0.8199 | 0.7979 |
71
+ | 0.2466 | 2.24 | 1400 | 0.4671 | 0.8601 | 0.8570 | 0.8683 | 0.8601 |
72
+ | 0.2456 | 2.4 | 1500 | 0.4479 | 0.8446 | 0.8371 | 0.8372 | 0.8446 |
73
+ | 0.2218 | 2.56 | 1600 | 0.5276 | 0.8342 | 0.8360 | 0.8483 | 0.8342 |
74
+ | 0.2019 | 2.72 | 1700 | 0.4866 | 0.8290 | 0.8289 | 0.8338 | 0.8290 |
75
+ | 0.1856 | 2.88 | 1800 | 0.4727 | 0.8342 | 0.8371 | 0.8426 | 0.8342 |
76
+ | 0.1614 | 3.04 | 1900 | 0.5576 | 0.8135 | 0.8126 | 0.8164 | 0.8135 |
77
+ | 0.0985 | 3.19 | 2000 | 0.5765 | 0.8394 | 0.8350 | 0.8413 | 0.8394 |
78
+ | 0.0826 | 3.35 | 2100 | 0.6482 | 0.8238 | 0.8062 | 0.7906 | 0.8238 |
79
+ | 0.1288 | 3.51 | 2200 | 0.6919 | 0.8238 | 0.8247 | 0.8415 | 0.8238 |
80
+ | 0.0808 | 3.67 | 2300 | 0.7174 | 0.8135 | 0.8141 | 0.8175 | 0.8135 |
81
+ | 0.0764 | 3.83 | 2400 | 0.6201 | 0.8446 | 0.8445 | 0.8454 | 0.8446 |
82
+ | 0.1346 | 3.99 | 2500 | 0.6639 | 0.8187 | 0.8165 | 0.8177 | 0.8187 |
83
+ | 0.0246 | 4.15 | 2600 | 0.7757 | 0.8135 | 0.8153 | 0.8177 | 0.8135 |
84
+ | 0.0059 | 4.31 | 2700 | 0.7933 | 0.8238 | 0.8195 | 0.8170 | 0.8238 |
85
+ | 0.0187 | 4.47 | 2800 | 0.8311 | 0.8238 | 0.8192 | 0.8153 | 0.8238 |
86
+ | 0.023 | 4.63 | 2900 | 0.8048 | 0.8238 | 0.8216 | 0.8217 | 0.8238 |
87
+ | 0.0186 | 4.79 | 3000 | 0.8510 | 0.8342 | 0.8301 | 0.8295 | 0.8342 |
88
+ | 0.0562 | 4.95 | 3100 | 0.7534 | 0.8446 | 0.8434 | 0.8429 | 0.8446 |
89
+ | 0.0046 | 5.11 | 3200 | 0.7817 | 0.8497 | 0.8480 | 0.8509 | 0.8497 |
90
+ | 0.0032 | 5.27 | 3300 | 0.7576 | 0.8446 | 0.8429 | 0.8447 | 0.8446 |
91
+ | 0.0031 | 5.43 | 3400 | 0.7692 | 0.8394 | 0.8374 | 0.8373 | 0.8394 |
92
+ | 0.0399 | 5.59 | 3500 | 0.7673 | 0.8394 | 0.8395 | 0.8415 | 0.8394 |
93
+ | 0.0052 | 5.75 | 3600 | 0.7809 | 0.8394 | 0.8388 | 0.8399 | 0.8394 |
94
+ | 0.0203 | 5.91 | 3700 | 0.7902 | 0.8446 | 0.8443 | 0.8449 | 0.8446 |
95
 
96
 
97
  ### Framework versions