beit-base-patch16-224-hasta-65-fold2

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: 0.6728
• Accuracy: 0.75

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	1.3013	0.3056
No log	1.7143	3	1.2799	0.2778
No log	2.8571	5	1.2588	0.3333
No log	4.0	7	1.1296	0.3889
No log	4.5714	8	1.1295	0.3611
1.1611	5.7143	10	1.2689	0.25
1.1611	6.8571	12	1.0895	0.3889
1.1611	8.0	14	1.0978	0.5
1.1611	8.5714	15	1.1168	0.5278
1.1611	9.7143	17	1.0844	0.4167
1.1611	10.8571	19	1.0476	0.5
0.9913	12.0	21	1.2315	0.4722
0.9913	12.5714	22	1.1444	0.4722
0.9913	13.7143	24	1.0242	0.5
0.9913	14.8571	26	1.0495	0.5278
0.9913	16.0	28	1.1234	0.4722
0.9913	16.5714	29	1.2332	0.5278
0.9206	17.7143	31	1.4389	0.3611
0.9206	18.8571	33	1.0300	0.5
0.9206	20.0	35	1.0028	0.5278
0.9206	20.5714	36	1.0322	0.5
0.9206	21.7143	38	1.0871	0.5278
0.7309	22.8571	40	0.9616	0.4722
0.7309	24.0	42	0.9571	0.5556
0.7309	24.5714	43	0.9855	0.5278
0.7309	25.7143	45	0.9598	0.5278
0.7309	26.8571	47	0.9774	0.5278
0.7309	28.0	49	0.9205	0.5556
0.6039	28.5714	50	0.9073	0.5556
0.6039	29.7143	52	0.8644	0.5833
0.6039	30.8571	54	0.8931	0.5833
0.6039	32.0	56	0.8686	0.6111
0.6039	32.5714	57	0.8381	0.5833
0.6039	33.7143	59	0.8658	0.5556
0.4784	34.8571	61	0.9915	0.5556
0.4784	36.0	63	0.7971	0.5833
0.4784	36.5714	64	0.7682	0.6111
0.4784	37.7143	66	0.9361	0.5833
0.4784	38.8571	68	0.9093	0.5833
0.4469	40.0	70	0.6728	0.75
0.4469	40.5714	71	0.6415	0.7222
0.4469	41.7143	73	0.7045	0.6667
0.4469	42.8571	75	0.8974	0.6389
0.4469	44.0	77	0.8032	0.6111
0.4469	44.5714	78	0.7134	0.6944
0.4329	45.7143	80	0.6975	0.7222
0.4329	46.8571	82	0.6758	0.7222
0.4329	48.0	84	0.8327	0.6111
0.4329	48.5714	85	0.9089	0.6111
0.4329	49.7143	87	0.9158	0.6111
0.4329	50.8571	89	0.8007	0.6389
0.4282	52.0	91	0.7363	0.6389
0.4282	52.5714	92	0.7378	0.6389
0.4282	53.7143	94	0.7449	0.6111
0.4282	54.8571	96	0.7605	0.6111
0.4282	56.0	98	0.7853	0.6111
0.4282	56.5714	99	0.7903	0.5833
0.3188	57.1429	100	0.7926	0.5833

Framework versions

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