metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_beit_large_sgd_0001_fold1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8714524207011686

smids_10x_beit_large_sgd_0001_fold1

This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3289
Accuracy: 0.8715

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9122	1.0	751	1.0277	0.4591
0.7509	2.0	1502	0.8635	0.6194
0.6317	3.0	2253	0.7448	0.7012
0.5452	4.0	3004	0.6612	0.7446
0.5954	5.0	3755	0.5972	0.7830
0.5075	6.0	4506	0.5495	0.7930
0.5045	7.0	5257	0.5158	0.8130
0.4666	8.0	6008	0.4891	0.8147
0.4159	9.0	6759	0.4686	0.8247
0.4231	10.0	7510	0.4504	0.8280
0.4497	11.0	8261	0.4359	0.8364
0.3539	12.0	9012	0.4229	0.8381
0.3554	13.0	9763	0.4122	0.8414
0.3441	14.0	10514	0.4038	0.8414
0.3331	15.0	11265	0.3962	0.8431
0.3376	16.0	12016	0.3885	0.8431
0.374	17.0	12767	0.3827	0.8431
0.3157	18.0	13518	0.3768	0.8464
0.3563	19.0	14269	0.3725	0.8514
0.3183	20.0	15020	0.3682	0.8548
0.2569	21.0	15771	0.3646	0.8598
0.312	22.0	16522	0.3608	0.8581
0.3262	23.0	17273	0.3576	0.8598
0.3722	24.0	18024	0.3550	0.8598
0.3339	25.0	18775	0.3524	0.8598
0.3725	26.0	19526	0.3497	0.8631
0.35	27.0	20277	0.3474	0.8664
0.3858	28.0	21028	0.3456	0.8648
0.3212	29.0	21779	0.3439	0.8664
0.3222	30.0	22530	0.3422	0.8681
0.2584	31.0	23281	0.3410	0.8664
0.3877	32.0	24032	0.3393	0.8698
0.3116	33.0	24783	0.3380	0.8698
0.3141	34.0	25534	0.3366	0.8715
0.3279	35.0	26285	0.3358	0.8681
0.2798	36.0	27036	0.3348	0.8715
0.3928	37.0	27787	0.3341	0.8715
0.3	38.0	28538	0.3331	0.8715
0.2471	39.0	29289	0.3324	0.8715
0.3456	40.0	30040	0.3317	0.8715
0.3078	41.0	30791	0.3311	0.8715
0.24	42.0	31542	0.3306	0.8715
0.289	43.0	32293	0.3302	0.8715
0.2977	44.0	33044	0.3297	0.8715
0.2559	45.0	33795	0.3294	0.8715
0.3508	46.0	34546	0.3292	0.8715
0.26	47.0	35297	0.3291	0.8715
0.3325	48.0	36048	0.3290	0.8715
0.2898	49.0	36799	0.3289	0.8715
0.2912	50.0	37550	0.3289	0.8715

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2