update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- image_folder
+metrics:
+- accuracy
+model-index:
+- name: Har_Finetuned-ViT-Hybrid
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: image_folder
+      type: image_folder
+      config: har
+      split: train
+      args: har
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8994708994708994
+---
+
+<!-- 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. -->
+
+# Har_Finetuned-ViT-Hybrid
+
+This model is a fine-tuned version of [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) on the image_folder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3383
+- Accuracy: 0.8995
+
+## 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: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.7923        | 1.0   | 167  | 0.4420          | 0.8698   |
+| 0.5555        | 2.0   | 334  | 0.3811          | 0.8820   |
+| 0.4734        | 3.0   | 501  | 0.3448          | 0.8958   |
+| 0.4019        | 4.0   | 668  | 0.3521          | 0.8926   |
+| 0.3622        | 5.0   | 835  | 0.3505          | 0.8926   |
+| 0.2921        | 6.0   | 1002 | 0.3383          | 0.8995   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.0
+- Datasets 2.1.0
+- Tokenizers 0.13.2