update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- cifar100
+metrics:
+- accuracy
+model-index:
+- name: swin-base-finetuned-cifar100
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: cifar100
+      type: cifar100
+      config: cifar100
+      split: train
+      args: cifar100
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9201
+---
+
+<!-- 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. -->
+
+# swin-base-finetuned-cifar100
+
+This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the cifar100 dataset.
+It achieves the following results on the evaluation set:
+- Accuracy: 0.9201
+- Loss: 0.3670
+
+## 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: 4e-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
+
+### Training results
+
+| Training Loss | Epoch | Step | Accuracy | Validation Loss |
+|:-------------:|:-----:|:----:|:--------:|:---------------:|
+| 0.3536        | 1.0   | 781  | 0.9052   | 0.3141          |
+| 0.3254        | 2.0   | 1562 | 0.9117   | 0.2991          |
+| 0.0936        | 3.0   | 2343 | 0.9138   | 0.3322          |
+| 0.1054        | 4.0   | 3124 | 0.9158   | 0.3483          |
+| 0.0269        | 5.0   | 3905 | 0.9201   | 0.3670          |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2