smids_10x_deit_tiny_rms_001_fold2

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: 2.1068
• Accuracy: 0.8020

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
0.8332	1.0	750	0.7567	0.6090
0.7395	2.0	1500	0.7599	0.6123
0.682	3.0	2250	0.6859	0.6905
0.725	4.0	3000	0.6463	0.7171
0.6632	5.0	3750	0.6560	0.7238
0.5777	6.0	4500	0.6347	0.7072
0.6357	7.0	5250	0.6141	0.7321
0.595	8.0	6000	0.6313	0.7121
0.5551	9.0	6750	0.6406	0.6955
0.5544	10.0	7500	0.5482	0.7720
0.5611	11.0	8250	0.5288	0.7704
0.6632	12.0	9000	0.5868	0.7537
0.5709	13.0	9750	0.6149	0.7288
0.4511	14.0	10500	0.4977	0.8020
0.4295	15.0	11250	0.5625	0.7770
0.4618	16.0	12000	0.5273	0.7837
0.4342	17.0	12750	0.5207	0.7804
0.4253	18.0	13500	0.5301	0.7720
0.4352	19.0	14250	0.5236	0.7754
0.418	20.0	15000	0.5318	0.7804
0.4496	21.0	15750	0.5216	0.7970
0.4003	22.0	16500	0.5391	0.7720
0.4411	23.0	17250	0.4904	0.8003
0.3266	24.0	18000	0.5436	0.7854
0.3733	25.0	18750	0.6780	0.7521
0.3536	26.0	19500	0.5100	0.8003
0.4154	27.0	20250	0.5545	0.8020
0.414	28.0	21000	0.5841	0.7937
0.3146	29.0	21750	0.5867	0.7887
0.3401	30.0	22500	0.5923	0.7987
0.2331	31.0	23250	0.6367	0.7837
0.238	32.0	24000	0.6276	0.8070
0.209	33.0	24750	0.6337	0.8070
0.2121	34.0	25500	0.6961	0.7854
0.2544	35.0	26250	0.7936	0.7870
0.2442	36.0	27000	0.7270	0.7970
0.2459	37.0	27750	0.7553	0.8020
0.1428	38.0	28500	0.8600	0.7987
0.0788	39.0	29250	0.9727	0.7937
0.1811	40.0	30000	1.0324	0.7937
0.1405	41.0	30750	1.0037	0.8103
0.1282	42.0	31500	1.1830	0.7937
0.0664	43.0	32250	1.2624	0.7970
0.04	44.0	33000	1.4942	0.7987
0.0582	45.0	33750	1.4631	0.8103
0.0738	46.0	34500	1.6687	0.8120
0.0282	47.0	35250	1.8321	0.8087
0.0021	48.0	36000	1.9181	0.8087
0.01	49.0	36750	2.0036	0.8037
0.0004	50.0	37500	2.1068	0.8020

Framework versions

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