metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-Trial007-YEL_STEM3
    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: 1

vit-base-patch16-224-Trial007-YEL_STEM3

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

Loss: 0.0840
Accuracy: 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: 60
eval_batch_size: 60
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 240
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
0.7133	0.89	2	0.6860	0.5926
0.6666	1.78	4	0.6151	0.7037
0.524	2.67	6	0.4915	0.8704
0.3759	4.0	9	0.3304	0.9074
0.3218	4.89	11	0.2177	0.9259
0.2764	5.78	13	0.1735	0.9630
0.2558	6.67	15	0.0840	1.0
0.7446	8.0	18	0.1256	0.9815
0.1251	8.89	20	0.0527	1.0
0.1491	9.78	22	0.0366	1.0
0.1559	10.67	24	0.0225	1.0
0.0916	12.0	27	0.0139	1.0
0.0751	12.89	29	0.0112	1.0
0.084	13.78	31	0.0102	1.0
0.0741	14.67	33	0.0103	1.0
0.065	16.0	36	0.0071	1.0
0.1019	16.89	38	0.0061	1.0
0.061	17.78	40	0.0052	1.0
0.0751	18.67	42	0.0045	1.0
0.0336	20.0	45	0.0064	1.0
0.0362	20.89	47	0.0038	1.0
0.073	21.78	49	0.0037	1.0
0.0748	22.67	51	0.0050	1.0
0.0543	24.0	54	0.0056	1.0
0.0408	24.89	56	0.0048	1.0
0.0729	25.78	58	0.0050	1.0
0.0638	26.67	60	0.0035	1.0
0.042	28.0	63	0.0029	1.0
0.0982	28.89	65	0.0025	1.0
0.0238	29.78	67	0.0023	1.0
0.0536	30.67	69	0.0032	1.0
0.1131	32.0	72	0.0029	1.0
0.0569	32.89	74	0.0030	1.0
0.0717	33.78	76	0.0034	1.0
0.0567	34.67	78	0.0037	1.0
0.0807	36.0	81	0.0039	1.0
0.0888	36.89	83	0.0039	1.0
0.0548	37.78	85	0.0039	1.0
0.078	38.67	87	0.0040	1.0
0.0453	40.0	90	0.0041	1.0
0.0442	40.89	92	0.0042	1.0
0.0425	41.78	94	0.0041	1.0
0.0619	42.67	96	0.0041	1.0
0.0437	44.0	99	0.0040	1.0
0.0552	44.44	100	0.0040	1.0

Framework versions

Transformers 4.30.0.dev0
Pytorch 1.12.1
Datasets 2.12.0
Tokenizers 0.13.1