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_sgd_001_fold2
    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: 0.8452579034941764

smids_1x_deit_small_sgd_001_fold2

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: 0.3804
Accuracy: 0.8453

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: 0.001
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
0.9829	1.0	75	0.9415	0.6140
0.8706	2.0	150	0.8332	0.6855
0.7459	3.0	225	0.7472	0.7105
0.699	4.0	300	0.6837	0.7404
0.6391	5.0	375	0.6363	0.7587
0.5631	6.0	450	0.5989	0.7687
0.5887	7.0	525	0.5694	0.7820
0.5519	8.0	600	0.5464	0.7887
0.4995	9.0	675	0.5237	0.7970
0.5219	10.0	750	0.5075	0.8053
0.4633	11.0	825	0.4946	0.8070
0.4238	12.0	900	0.4804	0.8220
0.4323	13.0	975	0.4699	0.8253
0.358	14.0	1050	0.4601	0.8253
0.3922	15.0	1125	0.4517	0.8270
0.412	16.0	1200	0.4446	0.8253
0.3525	17.0	1275	0.4384	0.8236
0.3851	18.0	1350	0.4333	0.8253
0.4196	19.0	1425	0.4291	0.8353
0.3588	20.0	1500	0.4237	0.8286
0.4139	21.0	1575	0.4192	0.8353
0.3218	22.0	1650	0.4155	0.8369
0.3303	23.0	1725	0.4117	0.8419
0.3481	24.0	1800	0.4077	0.8419
0.3241	25.0	1875	0.4056	0.8403
0.3326	26.0	1950	0.4042	0.8453
0.3492	27.0	2025	0.4005	0.8453
0.2987	28.0	2100	0.3985	0.8419
0.3361	29.0	2175	0.3960	0.8453
0.2986	30.0	2250	0.3933	0.8469
0.2629	31.0	2325	0.3933	0.8469
0.3171	32.0	2400	0.3920	0.8453
0.2746	33.0	2475	0.3904	0.8453
0.2943	34.0	2550	0.3897	0.8453
0.2828	35.0	2625	0.3874	0.8453
0.2865	36.0	2700	0.3865	0.8453
0.2715	37.0	2775	0.3856	0.8453
0.3146	38.0	2850	0.3843	0.8453
0.2703	39.0	2925	0.3840	0.8453
0.2829	40.0	3000	0.3831	0.8453
0.2957	41.0	3075	0.3831	0.8453
0.29	42.0	3150	0.3825	0.8469
0.2671	43.0	3225	0.3820	0.8469
0.262	44.0	3300	0.3814	0.8453
0.2814	45.0	3375	0.3812	0.8436
0.272	46.0	3450	0.3810	0.8436
0.2705	47.0	3525	0.3806	0.8453
0.2571	48.0	3600	0.3805	0.8453
0.267	49.0	3675	0.3804	0.8453
0.2797	50.0	3750	0.3804	0.8453

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0