smids_10x_deit_small_sgd_00001_fold4

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: 0.9309
• Accuracy: 0.5667

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
1.049	1.0	750	1.0657	0.4217
1.0271	2.0	1500	1.0619	0.4233
1.0309	3.0	2250	1.0577	0.4233
1.0685	4.0	3000	1.0531	0.4233
1.0213	5.0	3750	1.0484	0.425
1.0075	6.0	4500	1.0438	0.4267
1.0135	7.0	5250	1.0390	0.4283
1.0193	8.0	6000	1.0343	0.43
1.0172	9.0	6750	1.0296	0.4383
0.995	10.0	7500	1.0249	0.4417
0.9861	11.0	8250	1.0204	0.4467
0.9925	12.0	9000	1.0158	0.4533
0.9841	13.0	9750	1.0115	0.465
0.9738	14.0	10500	1.0072	0.4733
0.9779	15.0	11250	1.0030	0.4783
0.9393	16.0	12000	0.9988	0.485
0.968	17.0	12750	0.9949	0.485
0.9542	18.0	13500	0.9909	0.4883
0.9456	19.0	14250	0.9871	0.4917
0.9805	20.0	15000	0.9834	0.4967
0.9272	21.0	15750	0.9798	0.5
0.9402	22.0	16500	0.9763	0.5083
0.9463	23.0	17250	0.9729	0.5133
0.9349	24.0	18000	0.9697	0.515
0.9212	25.0	18750	0.9666	0.5167
0.9115	26.0	19500	0.9636	0.5183
0.9201	27.0	20250	0.9607	0.5217
0.9475	28.0	21000	0.9580	0.525
0.9135	29.0	21750	0.9554	0.5267
0.9341	30.0	22500	0.9529	0.53
0.9173	31.0	23250	0.9505	0.5317
0.9276	32.0	24000	0.9483	0.535
0.9211	33.0	24750	0.9462	0.5417
0.9232	34.0	25500	0.9443	0.5467
0.9171	35.0	26250	0.9425	0.5483
0.9007	36.0	27000	0.9408	0.5483
0.9143	37.0	27750	0.9393	0.555
0.8916	38.0	28500	0.9379	0.5567
0.8951	39.0	29250	0.9366	0.5567
0.9014	40.0	30000	0.9355	0.5567
0.8889	41.0	30750	0.9345	0.5583
0.8953	42.0	31500	0.9336	0.5583
0.9154	43.0	32250	0.9329	0.5583
0.8836	44.0	33000	0.9323	0.5633
0.8961	45.0	33750	0.9318	0.5667
0.8837	46.0	34500	0.9314	0.5667
0.8621	47.0	35250	0.9311	0.5667
0.8982	48.0	36000	0.9310	0.5667
0.8793	49.0	36750	0.9309	0.5667
0.8813	50.0	37500	0.9309	0.5667

Framework versions

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