metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: Skin_Cancer
          split: train
          args: Skin_Cancer
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7220338983050848

swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50

This model is a fine-tuned version of microsoft/swinv2-large-patch4-window12-192-22k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6967
Accuracy: 0.7220

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-06
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.005
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.97	9	1.6984	0.3729
No log	1.95	18	1.5150	0.4881
1.6944	2.92	27	1.3304	0.5390
1.6944	4.0	37	1.1761	0.6
1.3633	4.97	46	1.0588	0.6373
1.3633	5.95	55	0.9952	0.6475
1.1208	6.92	64	0.9326	0.6610
1.1208	8.0	74	0.8785	0.6712
0.9891	8.97	83	0.8478	0.6746
0.9891	9.95	92	0.8144	0.6847
0.9011	10.92	101	0.7774	0.7017
0.9011	12.0	111	0.7567	0.6983
0.8143	12.97	120	0.7525	0.6949
0.8143	13.95	129	0.7309	0.7051
0.8143	14.92	138	0.7141	0.7119
0.7926	16.0	148	0.7095	0.7186
0.7926	16.97	157	0.7057	0.7220
0.7439	17.95	166	0.6988	0.7220
0.7439	18.92	175	0.6967	0.7220
0.7533	19.46	180	0.6967	0.7220

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3