smids_10x_deit_tiny_rms_0001_fold4

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: 2.3327
• Accuracy: 0.8367

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
0.7568	1.0	750	0.7218	0.6717
0.6894	2.0	1500	0.6565	0.6583
0.6167	3.0	2250	0.6175	0.7017
0.6218	4.0	3000	0.6221	0.705
0.5637	5.0	3750	0.5565	0.7667
0.5037	6.0	4500	0.5481	0.7533
0.441	7.0	5250	0.5584	0.7667
0.5186	8.0	6000	0.5150	0.78
0.4475	9.0	6750	0.5478	0.775
0.4117	10.0	7500	0.5408	0.7867
0.4227	11.0	8250	0.4928	0.8017
0.4314	12.0	9000	0.5195	0.785
0.3606	13.0	9750	0.4998	0.8033
0.3206	14.0	10500	0.5323	0.8083
0.2628	15.0	11250	0.5344	0.79
0.2274	16.0	12000	0.5778	0.81
0.1649	17.0	12750	0.5559	0.8283
0.2197	18.0	13500	0.5838	0.8017
0.242	19.0	14250	0.5757	0.83
0.178	20.0	15000	0.6143	0.8183
0.1596	21.0	15750	0.6500	0.8267
0.1681	22.0	16500	0.7191	0.83
0.1356	23.0	17250	0.6652	0.83
0.154	24.0	18000	0.7572	0.835
0.0973	25.0	18750	0.7886	0.8283
0.1206	26.0	19500	0.9030	0.8033
0.0856	27.0	20250	1.0266	0.8083
0.0629	28.0	21000	0.8154	0.8333
0.0803	29.0	21750	1.0582	0.8133
0.0608	30.0	22500	1.1240	0.8317
0.0468	31.0	23250	1.1197	0.8183
0.0343	32.0	24000	1.2322	0.8217
0.0156	33.0	24750	1.3344	0.8367
0.0192	34.0	25500	1.3961	0.8133
0.0219	35.0	26250	1.5315	0.8033
0.0147	36.0	27000	1.5425	0.8233
0.0123	37.0	27750	1.6413	0.835
0.0089	38.0	28500	1.7045	0.8167
0.0003	39.0	29250	1.6054	0.8183
0.0102	40.0	30000	1.6942	0.825
0.0008	41.0	30750	1.7260	0.84
0.0077	42.0	31500	1.9643	0.8217
0.0048	43.0	32250	2.0335	0.825
0.0015	44.0	33000	2.2512	0.8367
0.0015	45.0	33750	2.1796	0.8333
0.0001	46.0	34500	2.2799	0.83
0.0	47.0	35250	2.2493	0.8317
0.0	48.0	36000	2.3177	0.8417
0.0	49.0	36750	2.3130	0.8317
0.0	50.0	37500	2.3327	0.8367

Framework versions

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