| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: flower_classification |
| | 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: 0.9706601466992665 |
| | - name: F1 |
| | type: f1 |
| | value: 0.97382606978311 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # flower_classification |
| | |
| | This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1638 |
| | - Accuracy: 0.9707 |
| | - F1: 0.9738 |
| | |
| | ## 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: 0.0005 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine_with_restarts |
| | - lr_scheduler_warmup_steps: 63 |
| | - num_epochs: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | | 2.134 | 1.0 | 205 | 0.8454 | 0.8582 | 0.8377 | |
| | | 0.6349 | 2.0 | 410 | 0.7229 | 0.8252 | 0.7947 | |
| | | 0.3946 | 3.0 | 615 | 0.6453 | 0.8521 | 0.8301 | |
| | | 0.2747 | 4.0 | 820 | 0.3665 | 0.9083 | 0.8901 | |
| | | 0.1668 | 5.0 | 1025 | 0.3964 | 0.8998 | 0.8692 | |
| | | 0.0767 | 6.0 | 1230 | 0.2997 | 0.9303 | 0.9282 | |
| | | 0.0205 | 7.0 | 1435 | 0.1774 | 0.9584 | 0.9590 | |
| | | 0.0066 | 8.0 | 1640 | 0.1467 | 0.9719 | 0.9732 | |
| | | 0.0027 | 9.0 | 1845 | 0.1571 | 0.9707 | 0.9716 | |
| | | 0.0026 | 10.0 | 2050 | 0.1603 | 0.9694 | 0.9709 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.3 |
| | - Pytorch 2.2.2 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
