metadata
license: apache-2.0
base_model: microsoft/swin-large-patch4-window12-384-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: microsoft/swin-large-patch4-window12-384-in22k
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: NIH-Xray
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.49376114081996436
microsoft/swin-large-patch4-window12-384-in22k
This model is a fine-tuned version of microsoft/swin-large-patch4-window12-384-in22k on the NIH-Xray dataset. It achieves the following results on the evaluation set:
- Loss: 3.7711
- Accuracy: 0.4938
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|---|---|---|---|---|
| 1.8318 | 0.9984 | 315 | 1.7651 | 0.5437 |
| 1.6067 | 2.0 | 631 | 1.6393 | 0.5455 |
| 1.406 | 2.9984 | 946 | 1.6472 | 0.5490 |
| 1.3983 | 4.0 | 1262 | 1.7344 | 0.5455 |
| 0.7272 | 4.9984 | 1577 | 2.1283 | 0.5258 |
| 0.3975 | 6.0 | 1893 | 2.5229 | 0.5134 |
| 0.2648 | 6.9984 | 2208 | 3.0333 | 0.5080 |
| 0.1232 | 8.0 | 2524 | 3.4626 | 0.5241 |
| 0.0873 | 8.9984 | 2839 | 3.6219 | 0.5027 |
| 0.0554 | 9.9842 | 3150 | 3.7711 | 0.4938 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1