metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: resnet-50-LongSleeveCleanedData
    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: 0.9787709497206704

resnet-50-LongSleeveCleanedData

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0889
Accuracy: 0.9788

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 7
total_train_batch_size: 56
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9906	0.99	143	1.0394	0.6134
0.7315	2.0	287	0.6790	0.7631
0.559	3.0	431	0.4735	0.8547
0.4905	4.0	575	0.3148	0.8983
0.3465	5.0	719	0.2225	0.9363
0.3372	6.0	863	0.1839	0.9486
0.3349	7.0	1007	0.1617	0.9587
0.3159	7.99	1150	0.1323	0.9620
0.2805	9.0	1294	0.1660	0.9587
0.2657	10.0	1438	0.1456	0.9531
0.2929	11.0	1582	0.1086	0.9698
0.2763	12.0	1726	0.0886	0.9765
0.2475	13.0	1870	0.1041	0.9732
0.2148	14.0	2014	0.0955	0.9777
0.209	14.99	2157	0.1061	0.9709
0.2408	16.0	2301	0.0784	0.9743
0.222	17.0	2445	0.0839	0.9698
0.208	18.0	2589	0.0873	0.9732
0.2214	19.0	2733	0.0889	0.9788
0.2375	19.88	2860	0.0864	0.9743

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3