beit-base-patch16-224-hasta-55-fold3

This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.7105
• Accuracy: 0.7222

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.5714	1	1.2994	0.3056
No log	1.7143	3	1.0652	0.3611
No log	2.8571	5	1.2855	0.3611
No log	4.0	7	1.0884	0.4167
No log	4.5714	8	1.0788	0.4167
1.1909	5.7143	10	1.0456	0.5278
1.1909	6.8571	12	0.9974	0.5833
1.1909	8.0	14	1.0068	0.5
1.1909	8.5714	15	0.9823	0.5556
1.1909	9.7143	17	0.9753	0.5
1.1909	10.8571	19	0.9455	0.5278
0.9469	12.0	21	0.9915	0.4444
0.9469	12.5714	22	0.9367	0.5
0.9469	13.7143	24	0.9860	0.5278
0.9469	14.8571	26	0.8634	0.5278
0.9469	16.0	28	0.8750	0.5278
0.9469	16.5714	29	0.8053	0.6667
0.9167	17.7143	31	0.8073	0.6111
0.9167	18.8571	33	0.9109	0.5556
0.9167	20.0	35	0.9607	0.4722
0.9167	20.5714	36	0.8576	0.5556
0.9167	21.7143	38	0.7514	0.6667
0.8799	22.8571	40	0.7743	0.6111
0.8799	24.0	42	0.7105	0.7222
0.8799	24.5714	43	0.6894	0.6944
0.8799	25.7143	45	0.6870	0.6667
0.8799	26.8571	47	0.7546	0.6667
0.8799	28.0	49	0.6499	0.6667
0.6763	28.5714	50	0.6387	0.6944
0.6763	29.7143	52	0.7299	0.7222
0.6763	30.8571	54	0.6783	0.6111
0.6763	32.0	56	0.7893	0.6667
0.6763	32.5714	57	0.8103	0.5556
0.6763	33.7143	59	0.7723	0.6389
0.593	34.8571	61	0.8221	0.6389
0.593	36.0	63	0.6759	0.6389
0.593	36.5714	64	0.6459	0.6944
0.593	37.7143	66	0.6829	0.6667
0.593	38.8571	68	0.6855	0.6667
0.5137	40.0	70	0.6838	0.6667
0.5137	40.5714	71	0.7038	0.7222
0.5137	41.7143	73	0.6592	0.6944
0.5137	42.8571	75	0.6782	0.6667
0.5137	44.0	77	0.7684	0.6389
0.5137	44.5714	78	0.7532	0.6111
0.4517	45.7143	80	0.7316	0.6389
0.4517	46.8571	82	0.6814	0.6389
0.4517	48.0	84	0.6643	0.6111
0.4517	48.5714	85	0.6613	0.6389
0.4517	49.7143	87	0.6539	0.6389
0.4517	50.8571	89	0.6436	0.6389
0.4126	52.0	91	0.6381	0.6667
0.4126	52.5714	92	0.6365	0.6667
0.4126	53.7143	94	0.6375	0.6389
0.4126	54.8571	96	0.6453	0.6111
0.4126	56.0	98	0.6486	0.6111
0.4126	56.5714	99	0.6486	0.6111
0.3715	57.1429	100	0.6486	0.6111

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1