smids_10x_deit_tiny_rms_0001_fold3

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: 1.2831
• Accuracy: 0.745

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
0.8666	1.0	750	0.8178	0.5867
0.793	2.0	1500	0.8394	0.5383
0.7315	3.0	2250	0.8051	0.6133
0.6465	4.0	3000	0.7374	0.65
0.703	5.0	3750	0.7241	0.6517
0.6607	6.0	4500	0.6935	0.6617
0.6906	7.0	5250	0.6781	0.675
0.673	8.0	6000	0.6701	0.7
0.5876	9.0	6750	0.6156	0.715
0.5761	10.0	7500	0.6686	0.6883
0.695	11.0	8250	0.6673	0.675
0.5527	12.0	9000	0.6193	0.7183
0.5532	13.0	9750	0.6407	0.6983
0.6398	14.0	10500	0.6327	0.7267
0.5686	15.0	11250	0.6250	0.71
0.6507	16.0	12000	0.6131	0.7183
0.586	17.0	12750	0.5959	0.7367
0.6263	18.0	13500	0.6433	0.7083
0.5943	19.0	14250	0.5766	0.7467
0.6095	20.0	15000	0.5801	0.7383
0.4915	21.0	15750	0.5843	0.7467
0.5994	22.0	16500	0.5711	0.74
0.4915	23.0	17250	0.5881	0.7367
0.5455	24.0	18000	0.5829	0.73
0.5646	25.0	18750	0.6056	0.73
0.4802	26.0	19500	0.5993	0.73
0.4066	27.0	20250	0.5797	0.7617
0.5295	28.0	21000	0.6131	0.7433
0.4838	29.0	21750	0.5976	0.7533
0.454	30.0	22500	0.5851	0.755
0.3428	31.0	23250	0.6240	0.745
0.3934	32.0	24000	0.6108	0.755
0.3564	33.0	24750	0.6563	0.755
0.4234	34.0	25500	0.6360	0.7633
0.3741	35.0	26250	0.6145	0.765
0.3785	36.0	27000	0.6637	0.7583
0.3282	37.0	27750	0.6548	0.7817
0.3768	38.0	28500	0.7250	0.7483
0.3263	39.0	29250	0.6603	0.7633
0.3862	40.0	30000	0.6936	0.7617
0.2705	41.0	30750	0.7486	0.7733
0.2694	42.0	31500	0.8322	0.7683
0.2855	43.0	32250	0.8068	0.7733
0.2669	44.0	33000	0.9199	0.755
0.2143	45.0	33750	0.9335	0.7667
0.1925	46.0	34500	1.0133	0.76
0.1987	47.0	35250	1.0665	0.745
0.1978	48.0	36000	1.1590	0.75
0.1441	49.0	36750	1.2474	0.7517
0.1372	50.0	37500	1.2831	0.745

Framework versions

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