Model save

README.md

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+---
+base_model: google/vit-base-patch16-224-in21k
+library_name: peft
+license: apache-2.0
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+tags:
+- generated_from_trainer
+model-index:
+- name: vit-base-patch16-224-in21k-finetuned-tekno23
+  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. -->
+
+# vit-base-patch16-224-in21k-finetuned-tekno23
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2583
+- Accuracy: 0.4014
+- F1: 0.3092
+- Precision: 0.3870
+- Recall: 0.4014
+
+## 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: 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: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.3662        | 1.0   | 421  | 1.3619          | 0.3190   | 0.1618 | 0.2852    | 0.3190 |
+| 1.3095        | 2.0   | 842  | 1.2970          | 0.3962   | 0.2933 | 0.4359    | 0.3962 |
+| 1.2683        | 3.0   | 1263 | 1.2687          | 0.4020   | 0.3081 | 0.3550    | 0.4020 |
+| 1.2524        | 4.0   | 1684 | 1.2576          | 0.4057   | 0.3098 | 0.3951    | 0.4057 |
+| 1.2781        | 5.0   | 2105 | 1.2583          | 0.4014   | 0.3092 | 0.3870    | 0.4014 |
+
+
+### Framework versions
+
+- PEFT 0.12.1.dev0
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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