File size: 3,786 Bytes
d7e761e daadd42 d7e761e |
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 |
---
license: apache-2.0
base_model: microsoft/swin-large-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Psoriasis-500-100aug-224-swin-large
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.845414847161572
---
<!-- 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. -->
# Psoriasis-500-100aug-224-swin-large
This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7503
- Accuracy: 0.8454
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4942 | 0.9973 | 92 | 0.6791 | 0.7825 |
| 0.2458 | 1.9946 | 184 | 0.6565 | 0.8087 |
| 0.0935 | 2.9919 | 276 | 0.6838 | 0.8140 |
| 0.056 | 4.0 | 369 | 0.8758 | 0.7913 |
| 0.0267 | 4.9973 | 461 | 0.7926 | 0.8245 |
| 0.0074 | 5.9946 | 553 | 0.7328 | 0.8437 |
| 0.0056 | 6.9919 | 645 | 0.7332 | 0.8480 |
| 0.0019 | 8.0 | 738 | 0.7667 | 0.8524 |
| 0.0013 | 8.9973 | 830 | 0.7548 | 0.8437 |
| 0.0006 | 9.9729 | 920 | 0.7503 | 0.8454 |
# Classification Report
| Class | Precision (%) | Recall (%) | F1-Score (%) | Support |
|---------------------|---------------|------------|--------------|---------|
| Abnormal | 68 | 81 | 74 | 108 |
| Erythrodermic | 94 | 76 | 84 | 100 |
| Guttate | 92 | 87 | 89 | 114 |
| Inverse | 92 | 93 | 92 | 108 |
| Nail | 86 | 84 | 85 | 99 |
| Normal | 85 | 87 | 86 | 82 |
| Not Define | 99 | 99 | 99 | 92 |
| Palm Soles | 79 | 80 | 80 | 102 |
| Plaque | 88 | 75 | 81 | 84 |
| Psoriatic Arthritis | 83 | 82 | 83 | 104 |
| Pustular | 77 | 84 | 80 | 112 |
| Scalp | 88 | 94 | 91 | 80 |
| **Accuracy** | | | **85** | 1185 |
| **Macro Avg** | **86** | **85** | **85** | 1185 |
| **Weighted Avg** | **86** | **85** | **85** | 1185 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|