metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_40x_deit_small_rms_001_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: 0.7804878048780488

hushem_40x_deit_small_rms_001_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: 1.6080
Accuracy: 0.7805

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
1.4202	1.0	220	1.3858	0.2439
1.4846	2.0	440	1.3050	0.3659
1.213	3.0	660	1.2527	0.3659
1.2436	4.0	880	1.2166	0.3659
1.1042	5.0	1100	1.0859	0.4878
1.1247	6.0	1320	1.0109	0.5854
1.1005	7.0	1540	1.0680	0.4634
1.0292	8.0	1760	0.9693	0.5366
0.9154	9.0	1980	1.0620	0.6829
0.8944	10.0	2200	0.7963	0.6585
0.9136	11.0	2420	1.0510	0.5854
0.8882	12.0	2640	0.9455	0.6098
0.8527	13.0	2860	0.7251	0.6098
0.7429	14.0	3080	0.7653	0.7805
0.6936	15.0	3300	0.7806	0.7561
0.7049	16.0	3520	1.3071	0.6585
0.6294	17.0	3740	0.5950	0.7561
0.6472	18.0	3960	0.4686	0.7561
0.6452	19.0	4180	0.5722	0.7561
0.5301	20.0	4400	0.8370	0.6341
0.5967	21.0	4620	0.5708	0.8537
0.4666	22.0	4840	0.3950	0.8293
0.5239	23.0	5060	0.5273	0.8049
0.5888	24.0	5280	0.4686	0.7561
0.5034	25.0	5500	0.5275	0.7561
0.5755	26.0	5720	0.4866	0.7805
0.5913	27.0	5940	0.8503	0.7805
0.405	28.0	6160	0.5132	0.8049
0.4508	29.0	6380	0.7683	0.7805
0.4334	30.0	6600	0.7092	0.8293
0.4219	31.0	6820	0.5130	0.7561
0.4182	32.0	7040	0.5153	0.8293
0.308	33.0	7260	0.5501	0.7561
0.3445	34.0	7480	0.6037	0.8049
0.2565	35.0	7700	0.5862	0.7805
0.2803	36.0	7920	0.7253	0.7805
0.3045	37.0	8140	0.9133	0.6829
0.2202	38.0	8360	0.8750	0.7073
0.222	39.0	8580	0.5361	0.7805
0.1816	40.0	8800	1.1395	0.7317
0.2213	41.0	9020	0.9746	0.7561
0.16	42.0	9240	1.0585	0.7317
0.1084	43.0	9460	0.8736	0.7073
0.1243	44.0	9680	1.1686	0.8049
0.1009	45.0	9900	0.9862	0.7561
0.096	46.0	10120	1.4994	0.6829
0.089	47.0	10340	1.4415	0.7561
0.0473	48.0	10560	1.6197	0.7805
0.0266	49.0	10780	1.5765	0.7805
0.0418	50.0	11000	1.6080	0.7805

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2