| | --- |
| | 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 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # microsoft/swin-large-patch4-window12-384-in22k |
| |
|
| | This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384-in22k](https://huggingface.co/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 |
| |
|
