metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-large-patch32-384-finetuned-melanoma
    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: 0.8272727272727273

vit-large-patch32-384-finetuned-melanoma

This model is a fine-tuned version of google/vit-large-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.0767
Accuracy: 0.8273

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0081	1.0	550	0.7650	0.68
0.7527	2.0	1100	0.6693	0.7364
0.6234	3.0	1650	0.6127	0.7709
2.6284	4.0	2200	0.6788	0.7655
0.1406	5.0	2750	0.6657	0.7836
0.317	6.0	3300	0.6936	0.78
2.5358	7.0	3850	0.7104	0.7909
1.5802	8.0	4400	0.6928	0.8
0.088	9.0	4950	0.8060	0.7982
0.0183	10.0	5500	0.7811	0.8091
0.0074	11.0	6050	0.7185	0.7945
0.0448	12.0	6600	0.8780	0.7909
0.4288	13.0	7150	0.8229	0.82
0.017	14.0	7700	0.7516	0.8182
0.0057	15.0	8250	0.7974	0.7964
1.7571	16.0	8800	0.7866	0.8218
1.3159	17.0	9350	0.8491	0.8073
1.649	18.0	9900	0.8432	0.7891
0.0014	19.0	10450	0.8870	0.82
0.002	20.0	11000	0.9460	0.8236
0.3717	21.0	11550	0.8866	0.8327
0.0025	22.0	12100	1.0287	0.8073
0.0094	23.0	12650	0.9696	0.8091
0.002	24.0	13200	0.9659	0.8018
0.1001	25.0	13750	0.9712	0.8327
0.2953	26.0	14300	1.0512	0.8236
0.0141	27.0	14850	1.0503	0.82
0.612	28.0	15400	1.2020	0.8109
0.0792	29.0	15950	1.0498	0.8364
0.0117	30.0	16500	1.0079	0.8327
0.0568	31.0	17050	1.0199	0.8255
0.0001	32.0	17600	1.0319	0.8291
0.075	33.0	18150	1.0427	0.8382
0.001	34.0	18700	1.1289	0.8382
0.0001	35.0	19250	1.0589	0.8364
0.0006	36.0	19800	1.0349	0.8236
0.0023	37.0	20350	1.1192	0.8273
0.0002	38.0	20900	1.0863	0.8273
0.2031	39.0	21450	1.0604	0.8255
0.0006	40.0	22000	1.0767	0.8273

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.0
Tokenizers 0.13.2