File size: 4,966 Bytes
30140db 6d24496 30140db 6d24496 30140db c09cf94 30140db c09cf94 cbe9796 30140db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-isic217
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5909090909090909
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-isic217
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3724
- Accuracy: 0.5909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.2679 | 0.9796 | 24 | 2.1550 | 0.0909 |
| 2.0504 | 2.0 | 49 | 2.0559 | 0.2727 |
| 1.8943 | 2.9796 | 73 | 2.0186 | 0.2273 |
| 1.5671 | 4.0 | 98 | 1.8154 | 0.2273 |
| 1.3425 | 4.9796 | 122 | 2.0475 | 0.2273 |
| 1.2758 | 6.0 | 147 | 2.1914 | 0.2273 |
| 0.9808 | 6.9796 | 171 | 2.0478 | 0.3636 |
| 0.7246 | 8.0 | 196 | 1.8840 | 0.4091 |
| 0.7323 | 8.9796 | 220 | 2.1831 | 0.4091 |
| 0.4881 | 10.0 | 245 | 2.2868 | 0.3636 |
| 0.4346 | 10.9796 | 269 | 2.2312 | 0.4545 |
| 0.5647 | 12.0 | 294 | 1.9897 | 0.4091 |
| 0.1464 | 12.9796 | 318 | 2.0579 | 0.4545 |
| 0.5575 | 14.0 | 343 | 2.1859 | 0.4545 |
| 0.3894 | 14.9796 | 367 | 2.7353 | 0.3636 |
| 0.4326 | 16.0 | 392 | 2.4455 | 0.3636 |
| 0.3715 | 16.9796 | 416 | 2.3104 | 0.5455 |
| 0.3966 | 18.0 | 441 | 2.4597 | 0.4545 |
| 0.1855 | 18.9796 | 465 | 2.3335 | 0.3636 |
| 0.1528 | 20.0 | 490 | 2.3630 | 0.4091 |
| 0.2036 | 20.9796 | 514 | 2.3520 | 0.4545 |
| 0.2026 | 22.0 | 539 | 2.7012 | 0.4091 |
| 0.2127 | 22.9796 | 563 | 2.3724 | 0.5909 |
| 0.2719 | 24.0 | 588 | 3.0376 | 0.3182 |
| 0.1292 | 24.9796 | 612 | 2.5684 | 0.5 |
| 0.2533 | 26.0 | 637 | 2.6974 | 0.4091 |
| 0.1947 | 26.9796 | 661 | 2.6957 | 0.4091 |
| 0.1805 | 28.0 | 686 | 2.8953 | 0.4091 |
| 0.1123 | 28.9796 | 710 | 2.8240 | 0.4091 |
| 0.2143 | 30.0 | 735 | 2.3880 | 0.4545 |
| 0.1845 | 30.9796 | 759 | 2.6072 | 0.3636 |
| 0.0921 | 32.0 | 784 | 2.7256 | 0.4545 |
| 0.0276 | 32.9796 | 808 | 2.4074 | 0.4091 |
| 0.0876 | 34.0 | 833 | 2.6043 | 0.4545 |
| 0.0253 | 34.9796 | 857 | 2.7620 | 0.4545 |
| 0.1904 | 36.0 | 882 | 2.6911 | 0.4091 |
| 0.072 | 36.9796 | 906 | 2.6528 | 0.4545 |
| 0.169 | 38.0 | 931 | 2.6454 | 0.4545 |
| 0.0978 | 38.9796 | 955 | 2.6269 | 0.5 |
| 0.069 | 40.0 | 980 | 2.4154 | 0.4545 |
| 0.0159 | 40.9796 | 1004 | 2.7026 | 0.4545 |
| 0.2046 | 42.0 | 1029 | 2.5213 | 0.4545 |
| 0.0329 | 42.9796 | 1053 | 2.6399 | 0.5 |
| 0.0166 | 44.0 | 1078 | 2.7787 | 0.4545 |
| 0.0812 | 44.9796 | 1102 | 2.8176 | 0.4545 |
| 0.0197 | 46.0 | 1127 | 2.8049 | 0.4545 |
| 0.0989 | 46.9796 | 1151 | 2.7479 | 0.4545 |
| 0.054 | 48.0 | 1176 | 2.7614 | 0.4545 |
| 0.1095 | 48.9796 | 1200 | 2.7604 | 0.5 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|