hushem_40x_deit_small_sgd_001_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: 0.9318
• 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
1.1891	1.0	215	1.3300	0.3778
0.9647	2.0	430	1.2794	0.4444
0.8581	3.0	645	1.2244	0.5111
0.699	4.0	860	1.1784	0.5333
0.6158	5.0	1075	1.1498	0.5111
0.5391	6.0	1290	1.1059	0.5556
0.4953	7.0	1505	1.0650	0.5333
0.4016	8.0	1720	1.0249	0.5556
0.3397	9.0	1935	0.9796	0.6222
0.3003	10.0	2150	0.9463	0.7111
0.246	11.0	2365	0.9270	0.7111
0.1949	12.0	2580	0.9025	0.7111
0.1895	13.0	2795	0.8872	0.7111
0.1659	14.0	3010	0.8723	0.7111
0.1576	15.0	3225	0.8544	0.7111
0.1305	16.0	3440	0.8521	0.7111
0.1123	17.0	3655	0.8414	0.7111
0.1025	18.0	3870	0.8453	0.7111
0.0749	19.0	4085	0.8597	0.7111
0.0854	20.0	4300	0.8467	0.7111
0.0788	21.0	4515	0.8314	0.7111
0.0675	22.0	4730	0.8392	0.7111
0.0523	23.0	4945	0.8293	0.7111
0.0556	24.0	5160	0.8555	0.7111
0.0483	25.0	5375	0.8566	0.7111
0.0417	26.0	5590	0.8533	0.7111
0.0397	27.0	5805	0.8560	0.7333
0.0302	28.0	6020	0.8587	0.7333
0.0286	29.0	6235	0.8633	0.7333
0.0386	30.0	6450	0.8691	0.7333
0.0212	31.0	6665	0.8693	0.7333
0.0221	32.0	6880	0.8714	0.7333
0.0198	33.0	7095	0.8818	0.7333
0.0189	34.0	7310	0.8880	0.7333
0.0167	35.0	7525	0.8939	0.7333
0.0198	36.0	7740	0.9010	0.7333
0.0157	37.0	7955	0.8988	0.7333
0.0177	38.0	8170	0.9154	0.7333
0.0136	39.0	8385	0.9094	0.7333
0.0108	40.0	8600	0.9213	0.7333
0.0119	41.0	8815	0.9173	0.7333
0.0127	42.0	9030	0.9219	0.7333
0.0095	43.0	9245	0.9256	0.7333
0.0124	44.0	9460	0.9223	0.7333
0.0112	45.0	9675	0.9246	0.7333
0.0112	46.0	9890	0.9266	0.7333
0.0102	47.0	10105	0.9301	0.7333
0.0105	48.0	10320	0.9338	0.7333
0.0119	49.0	10535	0.9314	0.7333
0.0144	50.0	10750	0.9318	0.7333

Framework versions

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