metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1
    results: []
datasets:
  - hongrui/mammogram_v_1
pipeline_tag: image-classification

vit-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1

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

Loss: 0.7419
Accuracy: 0.6991
F1: 0.6767
Precision: 0.6830
Recall: 0.6991

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.8576	1.0	171	0.8431	0.6678	0.6067	0.7751	0.6678
0.8297	2.0	342	0.7965	0.6791	0.6182	0.6758	0.6791
0.8303	3.0	513	0.7872	0.6842	0.6360	0.6704	0.6842
0.7814	4.0	684	0.7717	0.6843	0.6597	0.6601	0.6843
0.7768	5.0	855	0.7694	0.6906	0.6544	0.6775	0.6906
0.7415	6.0	1026	0.7572	0.6962	0.6718	0.6764	0.6962
0.7351	7.0	1197	0.7549	0.6922	0.6569	0.6648	0.6922
0.7197	8.0	1368	0.7479	0.6986	0.6855	0.6926	0.6986
0.7087	9.0	1539	0.7445	0.6979	0.6697	0.6792	0.6979
0.6977	10.0	1710	0.7419	0.6991	0.6767	0.6830	0.6991

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1