Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6990740740740741
+---
+
+<!-- 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-large-patch4-window12to24-192to384-22kto1k-ft-microbes
+
+This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0311
+- Accuracy: 0.6991
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 3.8445        | 0.98  | 15   | 2.8535          | 0.3194   |
+| 2.1358        | 1.97  | 30   | 1.9654          | 0.4491   |
+| 1.5947        | 2.95  | 45   | 1.4172          | 0.6204   |
+| 1.045         | 4.0   | 61   | 1.1698          | 0.6806   |
+| 0.985         | 4.98  | 76   | 1.1927          | 0.6852   |
+| 0.775         | 5.97  | 91   | 1.1012          | 0.6898   |
+| 0.7207        | 6.95  | 106  | 1.0311          | 0.7130   |
+| 0.6611        | 7.87  | 120  | 1.0311          | 0.6991   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cpu
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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