metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: >-
      vit-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST
    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:
              accuracy: 1
          - name: F1
            type: f1
            value:
              f1: 1
          - name: Precision
            type: precision
            value:
              precision: 1
          - name: Recall
            type: recall
            value:
              recall: 1

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

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Accuracy: {'accuracy': 1.0}
F1: {'f1': 1.0}
Precision: {'precision': 1.0}
Recall: {'recall': 1.0}

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0229	0.9973	274	0.0166	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0083	1.9982	549	0.0062	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0055	2.9991	824	0.0032	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0025	4.0	1099	0.0019	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.004	4.9973	1373	0.0013	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.001	5.9982	1648	0.0009	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0032	6.9991	1923	0.0014	{'accuracy': 0.9998862343572241}	{'f1': 0.9998861783406705}	{'precision': 0.9998887157801024}	{'recall': 0.9998836668217777}
0.0011	8.0	2198	0.0005	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0035	8.9973	2472	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0004	9.9982	2747	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0003	10.9991	3022	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0004	12.0	3297	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	12.9973	3571	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0005	13.9982	3846	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.006	14.9991	4121	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	16.0	4396	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	16.9973	4670	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	17.9982	4945	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0004	18.9991	5220	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	20.0	5495	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	20.9973	5769	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0012	21.9982	6044	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	22.9991	6319	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	24.0	6594	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	24.9973	6868	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	25.9982	7143	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	26.9991	7418	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	28.0	7693	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	28.9973	7967	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	29.9181	8220	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}

Framework versions

Transformers 4.43.3
Pytorch 2.3.1
Datasets 2.20.0
Tokenizers 0.19.1