Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swinv2-tiny-patch4-window16-256
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: swinv2-tiny-patch4-window16-256-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. -->
+
+# swinv2-tiny-patch4-window16-256-finetuned-tekno24
+
+This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2871
+- Accuracy: 0.4224
+- F1: 0.3135
+- Precision: 0.4313
+- Recall: 0.4224
+
+## 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: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.3705        | 0.9963 | 68   | 1.3584          | 0.3563   | 0.2590 | 0.2886    | 0.3563 |
+| 1.3515        | 1.9927 | 136  | 1.3392          | 0.3921   | 0.2606 | 0.3925    | 0.3921 |
+| 1.3498        | 2.9890 | 204  | 1.3301          | 0.3912   | 0.2501 | 0.4247    | 0.3912 |
+| 1.3351        | 4.0    | 273  | 1.3225          | 0.3930   | 0.2452 | 0.5371    | 0.3930 |
+| 1.3212        | 4.9963 | 341  | 1.3128          | 0.3949   | 0.2556 | 0.4641    | 0.3949 |
+| 1.3316        | 5.9927 | 409  | 1.3052          | 0.4004   | 0.2723 | 0.4129    | 0.4004 |
+| 1.3269        | 6.9890 | 477  | 1.2980          | 0.4068   | 0.2850 | 0.4305    | 0.4068 |
+| 1.3034        | 8.0    | 546  | 1.2927          | 0.4123   | 0.2924 | 0.4448    | 0.4123 |
+| 1.3165        | 8.9963 | 614  | 1.2884          | 0.4215   | 0.3096 | 0.4453    | 0.4215 |
+| 1.3306        | 9.9634 | 680  | 1.2871          | 0.4224   | 0.3135 | 0.4313    | 0.4224 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

runs/Sep04_09-49-45_38d13df12198/events.out.tfevents.1725443402.38d13df12198.737.1

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