smids_1x_deit_tiny_sgd_001_fold2

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.4027
• Accuracy: 0.8303

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
1.092	1.0	75	1.0391	0.4642
0.9734	2.0	150	0.9266	0.5641
0.8567	3.0	225	0.8357	0.6339
0.7624	4.0	300	0.7647	0.6705
0.6877	5.0	375	0.7050	0.7205
0.6614	6.0	450	0.6522	0.7488
0.6494	7.0	525	0.6133	0.7604
0.6282	8.0	600	0.5876	0.7687
0.5404	9.0	675	0.5590	0.7787
0.5855	10.0	750	0.5391	0.7837
0.5304	11.0	825	0.5290	0.7820
0.4323	12.0	900	0.5141	0.7854
0.4483	13.0	975	0.4996	0.7987
0.3743	14.0	1050	0.4908	0.7970
0.3865	15.0	1125	0.4830	0.8003
0.4258	16.0	1200	0.4748	0.8053
0.3735	17.0	1275	0.4651	0.8087
0.4081	18.0	1350	0.4581	0.8087
0.4212	19.0	1425	0.4559	0.8120
0.3826	20.0	1500	0.4471	0.8170
0.4327	21.0	1575	0.4442	0.8170
0.3388	22.0	1650	0.4401	0.8136
0.3617	23.0	1725	0.4342	0.8186
0.3721	24.0	1800	0.4302	0.8220
0.3408	25.0	1875	0.4281	0.8170
0.2889	26.0	1950	0.4258	0.8203
0.362	27.0	2025	0.4230	0.8220
0.3321	28.0	2100	0.4203	0.8220
0.3436	29.0	2175	0.4171	0.8270
0.3045	30.0	2250	0.4153	0.8286
0.2874	31.0	2325	0.4166	0.8270
0.2803	32.0	2400	0.4140	0.8286
0.2889	33.0	2475	0.4102	0.8303
0.3058	34.0	2550	0.4088	0.8303
0.2713	35.0	2625	0.4095	0.8303
0.2862	36.0	2700	0.4078	0.8303
0.2924	37.0	2775	0.4065	0.8319
0.3044	38.0	2850	0.4070	0.8319
0.2574	39.0	2925	0.4050	0.8319
0.2876	40.0	3000	0.4054	0.8319
0.3084	41.0	3075	0.4046	0.8319
0.3077	42.0	3150	0.4046	0.8319
0.2848	43.0	3225	0.4035	0.8319
0.2711	44.0	3300	0.4038	0.8303
0.2493	45.0	3375	0.4031	0.8319
0.2526	46.0	3450	0.4030	0.8303
0.2813	47.0	3525	0.4028	0.8319
0.2908	48.0	3600	0.4027	0.8303
0.2758	49.0	3675	0.4027	0.8303
0.3051	50.0	3750	0.4027	0.8303

Framework versions

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