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corranm/
test_model_94

generated_from_trainer

google/vit-base-patch16-224-in21k

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test_model_94

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the corranm/first_vote_100_per_new2 dataset. It achieves the following results on the evaluation set:

Loss: 1.8933
F1 Macro: 0.0863
F1 Micro: 0.2197
F1 Weighted: 0.1195
Precision Macro: 0.0630
Precision Micro: 0.2197
Precision Weighted: 0.0868
Recall Macro: 0.1568
Recall Micro: 0.2197
Recall Weighted: 0.2197
Accuracy: 0.2197

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	Precision Macro	Precision Micro	Precision Weighted	Recall Macro	Recall Micro	Recall Weighted	Accuracy
1.9541	0.8	3	1.9150	0.0426	0.1591	0.0609	0.0263	0.1591	0.0377	0.1111	0.1591	0.1591	0.1591
1.9037	1.8	6	1.8975	0.0848	0.2121	0.1175	0.0601	0.2121	0.0831	0.1520	0.2121	0.2121	0.2121

Framework versions

Transformers 4.48.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

corranm/test_model_94

test_model_94

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

corranm/
test_model_94