| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: msi-swinv2-tiny |
| | 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: 0.6404109589041096 |
| | - name: F1 |
| | type: f1 |
| | value: 0.5016949152542373 |
| | - name: Precision |
| | type: precision |
| | value: 0.6290224650880388 |
| | - name: Recall |
| | type: recall |
| | value: 0.41723721304873135 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # msi-swinv2-tiny |
| |
|
| | This model was trained from scratch on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7208 |
| | - Accuracy: 0.6404 |
| | - F1: 0.5017 |
| | - Precision: 0.6290 |
| | - Recall: 0.4172 |
| |
|
| | ## 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: 1e-06 |
| | - 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: 4 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.4992 | 1.0 | 2015 | 0.7072 | 0.6189 | 0.4517 | 0.6009 | 0.3619 | |
| | | 0.4581 | 2.0 | 4031 | 0.7145 | 0.6383 | 0.4787 | 0.6387 | 0.3828 | |
| | | 0.4229 | 3.0 | 6047 | 0.7146 | 0.6434 | 0.5077 | 0.6329 | 0.4238 | |
| | | 0.4096 | 4.0 | 8060 | 0.7208 | 0.6404 | 0.5017 | 0.6290 | 0.4172 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| |
|
