swin-tiny-patch4-window7-224-finetuned-aiornot-step

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1075
• Accuracy: 0.9613
• F1: 0.9606
• Log Loss: 1.3937

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• lr_scheduler_warmup_steps: 10
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Log Loss
0.5919	0.17	10	0.3100	0.8722	0.8663	4.6071
0.3175	0.34	20	0.2757	0.8861	0.8793	4.1038
0.2445	0.52	30	0.2021	0.9130	0.9092	3.1359
0.2117	0.69	40	0.1510	0.9377	0.9360	2.2455
0.2062	0.86	50	0.1535	0.9334	0.9311	2.4003
0.1815	1.03	60	0.1493	0.9382	0.9361	2.2261
0.1458	1.2	70	0.2196	0.9135	0.9089	3.1166
0.1507	1.37	80	0.1826	0.9243	0.9207	2.7294
0.1389	1.55	90	0.1396	0.9377	0.9356	2.2455
0.1331	1.72	100	0.2182	0.9125	0.9077	3.1553
0.1458	1.89	110	0.2661	0.9033	0.8978	3.4843
0.147	2.06	120	0.1068	0.9597	0.9588	1.4518
0.119	2.23	130	0.1284	0.9501	0.9484	1.8002
0.1045	2.4	140	0.1802	0.9291	0.9259	2.5552
0.1115	2.58	150	0.1434	0.9441	0.9420	2.0132
0.1108	2.75	160	0.1365	0.9441	0.9421	2.0132
0.1044	2.92	170	0.1488	0.9398	0.9375	2.1680

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.0
• Datasets 2.14.4
• Tokenizers 0.13.3