hushem_40x_deit_tiny_sgd_001_fold1

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: 0.9268
• Accuracy: 0.7333

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
1.1534	1.0	215	1.3121	0.3778
0.8744	2.0	430	1.2305	0.5111
0.7578	3.0	645	1.1023	0.5556
0.6354	4.0	860	0.9584	0.5778
0.4832	5.0	1075	0.8877	0.6444
0.4506	6.0	1290	0.8214	0.6889
0.3619	7.0	1505	0.8077	0.6889
0.3187	8.0	1720	0.7845	0.6667
0.2423	9.0	1935	0.7629	0.7111
0.2351	10.0	2150	0.7464	0.7333
0.2043	11.0	2365	0.7249	0.6889
0.1712	12.0	2580	0.7297	0.7111
0.1294	13.0	2795	0.7280	0.7333
0.1185	14.0	3010	0.7610	0.7333
0.1264	15.0	3225	0.7479	0.7333
0.0869	16.0	3440	0.7617	0.7333
0.0902	17.0	3655	0.7623	0.7333
0.0782	18.0	3870	0.7805	0.7333
0.071	19.0	4085	0.7715	0.7333
0.063	20.0	4300	0.7777	0.7333
0.0587	21.0	4515	0.7497	0.7333
0.0675	22.0	4730	0.7998	0.7333
0.0426	23.0	4945	0.8200	0.7333
0.0373	24.0	5160	0.8281	0.7111
0.0441	25.0	5375	0.8317	0.7111
0.0323	26.0	5590	0.8133	0.7111
0.0359	27.0	5805	0.8214	0.7111
0.0291	28.0	6020	0.8265	0.7111
0.0287	29.0	6235	0.8490	0.7111
0.0271	30.0	6450	0.8534	0.7111
0.0256	31.0	6665	0.8626	0.7111
0.0212	32.0	6880	0.8791	0.7111
0.0155	33.0	7095	0.8740	0.7333
0.0144	34.0	7310	0.8433	0.7333
0.0132	35.0	7525	0.8680	0.7333
0.015	36.0	7740	0.8880	0.7333
0.0129	37.0	7955	0.8931	0.7333
0.018	38.0	8170	0.8891	0.7333
0.0092	39.0	8385	0.9122	0.7333
0.0085	40.0	8600	0.9159	0.7333
0.0124	41.0	8815	0.9199	0.7333
0.0125	42.0	9030	0.9056	0.7333
0.0107	43.0	9245	0.9191	0.7333
0.0095	44.0	9460	0.9083	0.7333
0.0115	45.0	9675	0.9189	0.7333
0.0088	46.0	9890	0.9241	0.7333
0.0065	47.0	10105	0.9299	0.7333
0.007	48.0	10320	0.9257	0.7333
0.0129	49.0	10535	0.9260	0.7333
0.0229	50.0	10750	0.9268	0.7333

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2