Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24
+  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. -->
+
+# beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24
+
+This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0073
+- Accuracy: 0.5785
+- F1: 0.5641
+- Precision: 0.5641
+- Recall: 0.5785
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.4008        | 0.9855  | 17   | 1.2967          | 0.4059   | 0.3220 | 0.3791    | 0.4059 |
+| 1.2363        | 1.9710  | 34   | 1.1309          | 0.5032   | 0.4187 | 0.4871    | 0.5032 |
+| 1.1716        | 2.9565  | 51   | 1.0983          | 0.5161   | 0.4385 | 0.4610    | 0.5161 |
+| 1.1479        | 4.0     | 69   | 1.0550          | 0.5409   | 0.5014 | 0.5067    | 0.5409 |
+| 1.1058        | 4.9855  | 86   | 1.0397          | 0.5500   | 0.4942 | 0.5208    | 0.5500 |
+| 1.0656        | 5.9710  | 103  | 1.0558          | 0.5556   | 0.5396 | 0.5486    | 0.5556 |
+| 1.0328        | 6.9565  | 120  | 1.0216          | 0.5730   | 0.5465 | 0.5513    | 0.5730 |
+| 1.0116        | 8.0     | 138  | 1.0469          | 0.5363   | 0.5187 | 0.5119    | 0.5363 |
+| 1.012         | 8.9855  | 155  | 1.0216          | 0.5629   | 0.5226 | 0.5344    | 0.5629 |
+| 1.0076        | 9.9710  | 172  | 1.0186          | 0.5675   | 0.5275 | 0.5379    | 0.5675 |
+| 0.9714        | 10.9565 | 189  | 1.0205          | 0.5638   | 0.5499 | 0.5549    | 0.5638 |
+| 0.9843        | 12.0    | 207  | 1.0117          | 0.5657   | 0.5488 | 0.5495    | 0.5657 |
+| 0.9427        | 12.9855 | 224  | 1.0072          | 0.5785   | 0.5643 | 0.5602    | 0.5785 |
+| 0.9268        | 13.9710 | 241  | 1.0068          | 0.5785   | 0.5652 | 0.5621    | 0.5785 |
+| 0.9525        | 14.7826 | 255  | 1.0073          | 0.5785   | 0.5641 | 0.5641    | 0.5785 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

model.safetensors

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