beit-base-patch16-224-hasta-65-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: 0.9494
• Accuracy: 0.6111

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	1.2972	0.3333
No log	1.7143	3	1.1961	0.4167
No log	2.8571	5	1.5408	0.2778
No log	4.0	7	1.1727	0.3611
No log	4.5714	8	1.1418	0.3611
1.2077	5.7143	10	1.2295	0.2778
1.2077	6.8571	12	1.1358	0.4167
1.2077	8.0	14	1.1396	0.4444
1.2077	8.5714	15	1.1827	0.3611
1.2077	9.7143	17	1.0719	0.3889
1.2077	10.8571	19	1.0911	0.4167
1.004	12.0	21	1.0003	0.5278
1.004	12.5714	22	0.9457	0.5
1.004	13.7143	24	1.0182	0.5
1.004	14.8571	26	0.9877	0.5
1.004	16.0	28	0.9537	0.5278
1.004	16.5714	29	0.9310	0.5833
0.8983	17.7143	31	0.8763	0.5278
0.8983	18.8571	33	0.9626	0.5556
0.8983	20.0	35	1.0252	0.5833
0.8983	20.5714	36	1.0288	0.5278
0.8983	21.7143	38	1.0807	0.4722
0.7347	22.8571	40	0.9995	0.4444
0.7347	24.0	42	1.3380	0.4722
0.7347	24.5714	43	1.3829	0.4722
0.7347	25.7143	45	1.0370	0.4722
0.7347	26.8571	47	1.0372	0.5278
0.7347	28.0	49	0.9478	0.5278
0.6583	28.5714	50	0.9328	0.5833
0.6583	29.7143	52	1.0592	0.5278
0.6583	30.8571	54	0.9613	0.5278
0.6583	32.0	56	0.8315	0.5556
0.6583	32.5714	57	0.8749	0.5278
0.6583	33.7143	59	1.0390	0.5556
0.5633	34.8571	61	0.9446	0.5833
0.5633	36.0	63	0.9586	0.5833
0.5633	36.5714	64	0.9877	0.5833
0.5633	37.7143	66	1.0395	0.5833
0.5633	38.8571	68	0.9494	0.6111
0.4905	40.0	70	1.0124	0.5833
0.4905	40.5714	71	1.1537	0.5556
0.4905	41.7143	73	1.2837	0.5833
0.4905	42.8571	75	1.0821	0.5556
0.4905	44.0	77	1.0084	0.5556
0.4905	44.5714	78	1.0433	0.5556
0.4609	45.7143	80	1.2119	0.5556
0.4609	46.8571	82	1.2976	0.5556
0.4609	48.0	84	1.1444	0.5833
0.4609	48.5714	85	1.0444	0.5833
0.4609	49.7143	87	0.9338	0.5278
0.4609	50.8571	89	0.9501	0.5833
0.4106	52.0	91	1.0081	0.5833
0.4106	52.5714	92	1.0580	0.5833
0.4106	53.7143	94	1.1188	0.5833
0.4106	54.8571	96	1.1291	0.5833
0.4106	56.0	98	1.1156	0.6111
0.4106	56.5714	99	1.1099	0.6111
0.3784	57.1429	100	1.1064	0.6111

Framework versions

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