README.md

metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_small_rms_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: 0.9015025041736227

smids_10x_deit_small_rms_0001_fold1

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

• Loss: 1.0119
• Accuracy: 0.9015

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
0.3304	1.0	751	0.3159	0.8881
0.1492	2.0	1502	0.3656	0.8798
0.137	3.0	2253	0.4619	0.8831
0.0713	4.0	3004	0.5428	0.8798
0.0396	5.0	3755	0.4807	0.9098
0.041	6.0	4506	0.6616	0.8881
0.0505	7.0	5257	0.6229	0.9032
0.016	8.0	6008	0.6188	0.8865
0.0115	9.0	6759	0.6016	0.8915
0.0369	10.0	7510	0.5294	0.8965
0.0298	11.0	8261	0.5896	0.8865
0.0282	12.0	9012	0.6477	0.8815
0.0159	13.0	9763	0.5597	0.9032
0.0145	14.0	10514	0.5006	0.9032
0.0274	15.0	11265	0.6365	0.8932
0.0233	16.0	12016	0.5679	0.9032
0.0005	17.0	12767	0.7523	0.8815
0.0253	18.0	13518	0.6785	0.8915
0.0256	19.0	14269	0.6246	0.8815
0.0099	20.0	15020	0.6349	0.9082
0.0038	21.0	15771	0.5512	0.9115
0.0001	22.0	16522	0.6204	0.9032
0.0062	23.0	17273	0.7652	0.8932
0.0	24.0	18024	0.6455	0.9048
0.0	25.0	18775	0.8288	0.8932
0.0004	26.0	19526	0.7865	0.8982
0.0072	27.0	20277	0.8381	0.8965
0.0124	28.0	21028	0.6706	0.9082
0.0027	29.0	21779	0.7345	0.9115
0.0001	30.0	22530	0.8086	0.9032
0.0007	31.0	23281	0.9133	0.8948
0.0	32.0	24032	0.9315	0.8948
0.0005	33.0	24783	0.9041	0.8865
0.0002	34.0	25534	0.7984	0.9082
0.0	35.0	26285	0.7336	0.9199
0.0	36.0	27036	0.7739	0.9082
0.0	37.0	27787	0.7374	0.9149
0.0	38.0	28538	0.8856	0.8998
0.0	39.0	29289	0.7863	0.9115
0.0	40.0	30040	0.8280	0.9065
0.0	41.0	30791	0.8525	0.9065
0.0	42.0	31542	0.8579	0.9015
0.0	43.0	32293	0.9128	0.9048
0.0	44.0	33044	0.9440	0.8998
0.0	45.0	33795	0.9799	0.8998
0.0	46.0	34546	0.9704	0.9015
0.0	47.0	35297	0.9969	0.9015
0.0	48.0	36048	1.0043	0.9015
0.0	49.0	36799	1.0140	0.8998
0.0	50.0	37550	1.0119	0.9015

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2