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vit-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST

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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: vit-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value:
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+ accuracy: 1.0
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+ - name: F1
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+ type: f1
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+ value:
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+ f1: 1.0
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+ - name: Precision
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+ type: precision
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+ value:
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+ precision: 1.0
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+ - name: Recall
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+ type: recall
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+ value:
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+ recall: 1.0
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vit-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0000
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+ - Accuracy: {'accuracy': 1.0}
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+ - F1: {'f1': 1.0}
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+ - Precision: {'precision': 1.0}
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+ - Recall: {'recall': 1.0}
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
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+ | 0.0229 | 0.9973 | 274 | 0.0166 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0083 | 1.9982 | 549 | 0.0062 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0055 | 2.9991 | 824 | 0.0032 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0025 | 4.0 | 1099 | 0.0019 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.004 | 4.9973 | 1373 | 0.0013 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.001 | 5.9982 | 1648 | 0.0009 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0032 | 6.9991 | 1923 | 0.0014 | {'accuracy': 0.9998862343572241} | {'f1': 0.9998861783406705} | {'precision': 0.9998887157801024} | {'recall': 0.9998836668217777} |
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+ | 0.0011 | 8.0 | 2198 | 0.0005 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0035 | 8.9973 | 2472 | 0.0004 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0004 | 9.9982 | 2747 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0003 | 10.9991 | 3022 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0004 | 12.0 | 3297 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0002 | 12.9973 | 3571 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0005 | 13.9982 | 3846 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.006 | 14.9991 | 4121 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 16.0 | 4396 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 16.9973 | 4670 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 17.9982 | 4945 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0004 | 18.9991 | 5220 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 20.0 | 5495 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 20.9973 | 5769 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0012 | 21.9982 | 6044 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 22.9991 | 6319 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 24.0 | 6594 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 24.9973 | 6868 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0002 | 25.9982 | 7143 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 26.9991 | 7418 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 28.0 | 7693 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 28.9973 | 7967 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+ | 0.0001 | 29.9181 | 8220 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.3
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+ - Pytorch 2.3.1
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1