smids_1x_deit_small_adamax_00001_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: 0.7991
• Accuracy: 0.8569

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
0.5561	1.0	75	0.5421	0.7804
0.3586	2.0	150	0.4304	0.8286
0.3328	3.0	225	0.3782	0.8586
0.266	4.0	300	0.3735	0.8536
0.2451	5.0	375	0.3596	0.8536
0.2001	6.0	450	0.3408	0.8669
0.1658	7.0	525	0.3450	0.8552
0.1239	8.0	600	0.3523	0.8519
0.0994	9.0	675	0.3736	0.8602
0.0972	10.0	750	0.3715	0.8636
0.0323	11.0	825	0.4031	0.8569
0.0293	12.0	900	0.4332	0.8586
0.0328	13.0	975	0.4440	0.8636
0.0162	14.0	1050	0.4947	0.8552
0.02	15.0	1125	0.5267	0.8586
0.014	16.0	1200	0.5586	0.8586
0.0039	17.0	1275	0.5843	0.8536
0.0174	18.0	1350	0.6241	0.8586
0.003	19.0	1425	0.6332	0.8502
0.0035	20.0	1500	0.6412	0.8569
0.0013	21.0	1575	0.6514	0.8519
0.0012	22.0	1650	0.6772	0.8502
0.0007	23.0	1725	0.6821	0.8502
0.0007	24.0	1800	0.7040	0.8569
0.0006	25.0	1875	0.6982	0.8569
0.0005	26.0	1950	0.7116	0.8519
0.0162	27.0	2025	0.7266	0.8602
0.0086	28.0	2100	0.7241	0.8536
0.0081	29.0	2175	0.7251	0.8602
0.0111	30.0	2250	0.7386	0.8602
0.0161	31.0	2325	0.7520	0.8586
0.0003	32.0	2400	0.7496	0.8519
0.0003	33.0	2475	0.7540	0.8552
0.0003	34.0	2550	0.7601	0.8602
0.0123	35.0	2625	0.7663	0.8519
0.0002	36.0	2700	0.7752	0.8586
0.0002	37.0	2775	0.7743	0.8502
0.0002	38.0	2850	0.7784	0.8519
0.0004	39.0	2925	0.7826	0.8536
0.0002	40.0	3000	0.7838	0.8536
0.0044	41.0	3075	0.7838	0.8586
0.0002	42.0	3150	0.7911	0.8552
0.001	43.0	3225	0.7929	0.8569
0.0106	44.0	3300	0.7934	0.8552
0.0002	45.0	3375	0.7947	0.8552
0.0002	46.0	3450	0.7979	0.8536
0.001	47.0	3525	0.7983	0.8536
0.0002	48.0	3600	0.7989	0.8569
0.0022	49.0	3675	0.7991	0.8569
0.0004	50.0	3750	0.7991	0.8569

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0