Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: timm/levit_128.fb_dist_in1k
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: levit_128.fb_dist_in1k-finetuned-stroke-binary
+  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. -->
+
+# levit_128.fb_dist_in1k-finetuned-stroke-binary
+
+This model is a fine-tuned version of [timm/levit_128.fb_dist_in1k](https://huggingface.co/timm/levit_128.fb_dist_in1k) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Accuracy: 0.6974
+- F1: 0.7014
+- Precision: 0.7298
+- Recall: 0.6974
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 36
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.695         | 0.6202 | 100  | nan             | 0.5364   | 0.5421 | 0.5541    | 0.5364 |
+| 0.6804        | 1.2357 | 200  | nan             | 0.5798   | 0.5833 | 0.6278    | 0.5798 |
+| 0.6821        | 1.8558 | 300  | nan             | 0.6232   | 0.6280 | 0.6413    | 0.6232 |
+| 0.6726        | 2.4713 | 400  | nan             | 0.6671   | 0.6711 | 0.6829    | 0.6671 |
+| 0.6546        | 3.0868 | 500  | nan             | 0.7024   | 0.7021 | 0.7018    | 0.7024 |
+| 0.647         | 3.7070 | 600  | nan             | 0.7065   | 0.7093 | 0.7159    | 0.7065 |
+| 0.6263        | 4.3225 | 700  | nan             | 0.6956   | 0.6991 | 0.7096    | 0.6956 |
+| 0.6112        | 4.9426 | 800  | nan             | 0.6766   | 0.6807 | 0.7123    | 0.6766 |
+| 0.5704        | 5.5581 | 900  | nan             | 0.6974   | 0.7014 | 0.7298    | 0.6974 |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.0
+- Tokenizers 0.21.0

model.safetensors

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