smids_1x_deit_tiny_rms_0001_fold5

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: 1.2046
• Accuracy: 0.8517

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: 0.0001
• 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
0.7851	1.0	75	0.8659	0.64
0.6518	2.0	150	0.7541	0.6467
0.4507	3.0	225	0.6126	0.755
0.4597	4.0	300	0.4698	0.805
0.3528	5.0	375	0.4309	0.835
0.2717	6.0	450	0.4110	0.8517
0.2211	7.0	525	0.5132	0.8283
0.1873	8.0	600	0.5255	0.835
0.1509	9.0	675	0.5409	0.85
0.06	10.0	750	0.7466	0.8333
0.1297	11.0	825	0.8027	0.835
0.0789	12.0	900	0.7518	0.8417
0.1522	13.0	975	0.7901	0.8533
0.0628	14.0	1050	0.8326	0.845
0.0732	15.0	1125	0.9433	0.8317
0.0276	16.0	1200	0.9028	0.845
0.0402	17.0	1275	0.8882	0.8617
0.0561	18.0	1350	0.9516	0.8367
0.0072	19.0	1425	1.0341	0.8467
0.0251	20.0	1500	1.0436	0.8433
0.0171	21.0	1575	0.8887	0.855
0.0141	22.0	1650	0.9265	0.8517
0.0297	23.0	1725	1.1359	0.8383
0.0008	24.0	1800	1.0337	0.8567
0.0322	25.0	1875	0.8913	0.87
0.0416	26.0	1950	0.9175	0.84
0.0268	27.0	2025	0.9551	0.86
0.0237	28.0	2100	1.0150	0.8533
0.0252	29.0	2175	0.8872	0.8617
0.0035	30.0	2250	0.9489	0.8633
0.0155	31.0	2325	1.0473	0.8417
0.0007	32.0	2400	0.9648	0.8533
0.0102	33.0	2475	1.0603	0.8517
0.0	34.0	2550	1.0445	0.8533
0.0057	35.0	2625	1.0369	0.8467
0.0	36.0	2700	1.0577	0.8517
0.004	37.0	2775	1.0782	0.845
0.0033	38.0	2850	1.1658	0.8433
0.0001	39.0	2925	1.0942	0.8533
0.0	40.0	3000	1.1718	0.8467
0.0038	41.0	3075	1.1726	0.855
0.0	42.0	3150	1.1472	0.85
0.008	43.0	3225	1.1850	0.8517
0.0008	44.0	3300	1.1576	0.845
0.0022	45.0	3375	1.1935	0.855
0.0	46.0	3450	1.1973	0.8533
0.0056	47.0	3525	1.2032	0.8533
0.0051	48.0	3600	1.2041	0.8533
0.0	49.0	3675	1.2053	0.8517
0.0043	50.0	3750	1.2046	0.8517

Framework versions

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