File size: 2,299 Bytes
93baa67 |
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 |
---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3
results: []
---
<!-- 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. -->
# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0112
- Accuracy: 0.9951
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1885 | 1.0 | 114 | 0.8718 | 0.6593 |
| 0.7037 | 2.0 | 228 | 0.4208 | 0.8637 |
| 0.5085 | 2.99 | 342 | 0.3446 | 0.8744 |
| 0.2874 | 4.0 | 457 | 0.2027 | 0.9327 |
| 0.355 | 5.0 | 571 | 0.1666 | 0.9401 |
| 0.2493 | 6.0 | 685 | 0.0969 | 0.9655 |
| 0.1909 | 6.99 | 799 | 0.0558 | 0.9836 |
| 0.1821 | 8.0 | 914 | 0.0412 | 0.9901 |
| 0.1853 | 9.0 | 1028 | 0.0239 | 0.9943 |
| 0.0666 | 9.98 | 1140 | 0.0112 | 0.9951 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|