yotasr/Smart_Tour_Guide_CairoVersion

1---2license: apache-2.03base_model: google/vit-base-patch16-224-in21k4tags:5- generated_from_trainer6datasets:7- imagefolder8metrics:9- accuracy10model-index:11- name: vit-base-patch16-224-in21k-Smart_Tour_CarioVersion12  results:13  - task:14      name: Image Classification15      type: image-classification16    dataset:17      name: imagefolder18      type: imagefolder19      config: default20      split: train21      args: default22    metrics:23    - name: Accuracy24      type: accuracy25      value: 0.997922725384295826---27 28<!-- This model card has been generated automatically according to the information the Trainer had access to. You29should probably proofread and complete it, then remove this comment. -->30 31# vit-base-patch16-224-in21k-Smart_Tour_CarioVersion32 33This 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.34It achieves the following results on the evaluation set:35- Loss: 0.375036- Accuracy: 0.997937 38## Model description39 40More information needed41 42## Intended uses & limitations43 44More information needed45 46## Training and evaluation data47 48More information needed49 50## Training procedure51 52### Training hyperparameters53 54The following hyperparameters were used during training:55- learning_rate: 5e-0556- train_batch_size: 3257- eval_batch_size: 3258- seed: 4259- gradient_accumulation_steps: 460- total_train_batch_size: 12861- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0862- lr_scheduler_type: linear63- lr_scheduler_warmup_ratio: 0.164- num_epochs: 365 66### Training results67 68| Training Loss | Epoch | Step | Validation Loss | Accuracy |69|:-------------:|:-----:|:----:|:---------------:|:--------:|70| 1.0618        | 1.0   | 75   | 0.8609          | 0.9842   |71| 0.4871        | 1.99  | 150  | 0.4370          | 0.9979   |72| 0.4088        | 2.99  | 225  | 0.3750          | 0.9979   |73 74 75### Framework versions76 77- Transformers 4.38.278- Pytorch 2.2.1+cu12179- Datasets 2.18.080- Tokenizers 0.15.281