metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_small_rms_00001_fold5
    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: 0.88

smids_1x_deit_small_rms_00001_fold5

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: 0.9281
Accuracy: 0.88

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: 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
0.3852	1.0	75	0.3081	0.87
0.2965	2.0	150	0.3016	0.8733
0.1467	3.0	225	0.3200	0.8783
0.1384	4.0	300	0.3262	0.8833
0.0702	5.0	375	0.3415	0.8817
0.0486	6.0	450	0.4818	0.8817
0.0342	7.0	525	0.4838	0.8817
0.0455	8.0	600	0.6047	0.8717
0.0096	9.0	675	0.5775	0.8817
0.028	10.0	750	0.6719	0.875
0.0419	11.0	825	0.6284	0.8833
0.0004	12.0	900	0.6384	0.8817
0.0259	13.0	975	0.6301	0.875
0.03	14.0	1050	0.6619	0.8733
0.0082	15.0	1125	0.8292	0.8667
0.0001	16.0	1200	0.7120	0.88
0.005	17.0	1275	0.7140	0.8867
0.028	18.0	1350	0.8747	0.865
0.0095	19.0	1425	0.8049	0.8767
0.0001	20.0	1500	0.7748	0.8767
0.0085	21.0	1575	0.7202	0.885
0.0152	22.0	1650	0.8388	0.875
0.0057	23.0	1725	0.8400	0.8733
0.0001	24.0	1800	0.8934	0.8717
0.0082	25.0	1875	0.8430	0.8783
0.0001	26.0	1950	0.8852	0.8783
0.008	27.0	2025	0.8664	0.8767
0.0113	28.0	2100	0.8872	0.88
0.0078	29.0	2175	0.8576	0.8817
0.0049	30.0	2250	0.8872	0.88
0.0	31.0	2325	0.9217	0.8733
0.0	32.0	2400	0.8681	0.8833
0.0081	33.0	2475	0.9201	0.8783
0.0	34.0	2550	0.9023	0.8767
0.0058	35.0	2625	0.9043	0.8767
0.0	36.0	2700	0.9027	0.88
0.0029	37.0	2775	0.9082	0.88
0.0	38.0	2850	0.9260	0.8767
0.0	39.0	2925	0.9311	0.8783
0.0	40.0	3000	0.9195	0.8767
0.0028	41.0	3075	0.9229	0.8767
0.0	42.0	3150	0.9218	0.8783
0.0075	43.0	3225	0.9281	0.8767
0.0	44.0	3300	0.9291	0.8767
0.0025	45.0	3375	0.9268	0.8783
0.0	46.0	3450	0.9285	0.88
0.0049	47.0	3525	0.9282	0.88
0.0048	48.0	3600	0.9283	0.88
0.0	49.0	3675	0.9284	0.88
0.0043	50.0	3750	0.9281	0.88

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0