Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: facebook/vit-msn-small
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-msn-small-corect_deepcleaned_dataset_lateral_flow_ivalidation
+  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: 0.8879227053140096
+---
+
+<!-- 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-msn-small-corect_deepcleaned_dataset_lateral_flow_ivalidation
+
+This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3520
+- Accuracy: 0.8879
+
+## 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: 20
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|
+| No log        | 0.9231  | 3    | 0.3933          | 0.8928   |
+| No log        | 1.8462  | 6    | 0.4167          | 0.9188   |
+| No log        | 2.7692  | 9    | 0.9331          | 0.4966   |
+| 0.615         | 4.0     | 13   | 0.3085          | 0.9179   |
+| 0.615         | 4.9231  | 16   | 0.2210          | 0.9333   |
+| 0.615         | 5.8462  | 19   | 0.2106          | 0.9391   |
+| 0.3763        | 6.7692  | 22   | 0.1871          | 0.9498   |
+| 0.3763        | 8.0     | 26   | 0.2043          | 0.9372   |
+| 0.3763        | 8.9231  | 29   | 0.3121          | 0.8889   |
+| 0.3511        | 9.8462  | 32   | 0.2015          | 0.9314   |
+| 0.3511        | 10.7692 | 35   | 0.4485          | 0.8377   |
+| 0.3511        | 12.0    | 39   | 0.2445          | 0.9285   |
+| 0.2962        | 12.9231 | 42   | 0.3045          | 0.9053   |
+| 0.2962        | 13.8462 | 45   | 0.3915          | 0.8715   |
+| 0.2962        | 14.7692 | 48   | 0.3165          | 0.8937   |
+| 0.2553        | 16.0    | 52   | 0.2823          | 0.9082   |
+| 0.2553        | 16.9231 | 55   | 0.3504          | 0.8870   |
+| 0.2553        | 17.8462 | 58   | 0.3679          | 0.8870   |
+| 0.2601        | 18.4615 | 60   | 0.3520          | 0.8879   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.19.1

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