update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-in21k_Human_Activity_Recognition
+  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: 0.8380952380952381
+---
+
+<!-- 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-base-patch16-224-in21k_Human_Activity_Recognition
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7403
+- Accuracy: 0.8381
+- Weighted f1: 0.8388
+- Micro f1: 0.8381
+- Macro f1: 0.8394
+- Weighted recall: 0.8381
+- Micro recall: 0.8381
+- Macro recall: 0.8390
+- Weighted precision: 0.8421
+- Micro precision: 0.8381
+- Macro precision: 0.8424
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 1.0814        | 1.0   | 630  | 0.7368          | 0.7794   | 0.7795      | 0.7794   | 0.7798   | 0.7794          | 0.7794       | 0.7797       | 0.7896             | 0.7794          | 0.7896          |
+| 0.5149        | 2.0   | 1260 | 0.6439          | 0.8060   | 0.8049      | 0.8060   | 0.8036   | 0.8060          | 0.8060       | 0.8051       | 0.8136             | 0.8060          | 0.8130          |
+| 0.3023        | 3.0   | 1890 | 0.7026          | 0.8254   | 0.8272      | 0.8254   | 0.8278   | 0.8254          | 0.8254       | 0.8256       | 0.8335             | 0.8254          | 0.8345          |
+| 0.0507        | 4.0   | 2520 | 0.7414          | 0.8317   | 0.8342      | 0.8317   | 0.8348   | 0.8317          | 0.8317       | 0.8321       | 0.8427             | 0.8317          | 0.8438          |
+| 0.0128        | 5.0   | 3150 | 0.7403          | 0.8381   | 0.8388      | 0.8381   | 0.8394   | 0.8381          | 0.8381       | 0.8390       | 0.8421             | 0.8381          | 0.8424          |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.12.1
+- Datasets 2.8.0
+- Tokenizers 0.12.1