smids_3x_deit_small_rms_001_fold4

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: 1.1078
• Accuracy: 0.7533

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
1.1005	1.0	225	0.9456	0.5067
0.8365	2.0	450	0.8727	0.46
0.7918	3.0	675	0.7967	0.555
0.7924	4.0	900	0.7472	0.62
0.8029	5.0	1125	0.7002	0.6433
0.7311	6.0	1350	0.6514	0.685
0.6939	7.0	1575	0.6643	0.7133
0.6634	8.0	1800	0.6358	0.715
0.6019	9.0	2025	0.6144	0.7067
0.6316	10.0	2250	0.6281	0.6967
0.6363	11.0	2475	0.6280	0.7233
0.645	12.0	2700	0.5846	0.7433
0.6675	13.0	2925	0.5877	0.7333
0.5165	14.0	3150	0.5728	0.75
0.52	15.0	3375	0.6706	0.73
0.5715	16.0	3600	0.6345	0.6983
0.4691	17.0	3825	0.6573	0.7167
0.5674	18.0	4050	0.5616	0.7633
0.5252	19.0	4275	0.5645	0.755
0.4602	20.0	4500	0.6017	0.755
0.4817	21.0	4725	0.5934	0.73
0.5616	22.0	4950	0.5845	0.7583
0.4883	23.0	5175	0.6144	0.7483
0.4781	24.0	5400	0.6001	0.7517
0.4229	25.0	5625	0.5953	0.77
0.4499	26.0	5850	0.6184	0.7767
0.4522	27.0	6075	0.6104	0.7433
0.4849	28.0	6300	0.6429	0.745
0.3967	29.0	6525	0.6127	0.7533
0.429	30.0	6750	0.6125	0.775
0.3969	31.0	6975	0.6078	0.7733
0.3935	32.0	7200	0.6333	0.7517
0.3619	33.0	7425	0.6571	0.795
0.4192	34.0	7650	0.6837	0.7833
0.4245	35.0	7875	0.6680	0.76
0.3704	36.0	8100	0.6540	0.78
0.393	37.0	8325	0.6566	0.7867
0.314	38.0	8550	0.7028	0.7783
0.3398	39.0	8775	0.7698	0.7667
0.3529	40.0	9000	0.7537	0.7633
0.3444	41.0	9225	0.8464	0.775
0.2933	42.0	9450	0.7973	0.7717
0.2384	43.0	9675	0.9278	0.765
0.3008	44.0	9900	0.9100	0.7767
0.2711	45.0	10125	0.9061	0.7683
0.3037	46.0	10350	0.9619	0.7783
0.2347	47.0	10575	1.0097	0.765
0.2672	48.0	10800	1.0493	0.75
0.2045	49.0	11025	1.0959	0.76
0.1823	50.0	11250	1.1078	0.7533

Framework versions

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