segformer-class-classWeights-augmentation

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

• Loss: 0.1855
• Accuracy: 0.9655
• F1: 0.9647
• Precision: 0.9674
• Recall: 0.9655
• Learning Rate: 0.0000

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: 10
• eval_batch_size: 10
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 40
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Rate
No log	0.89	6	0.1113	0.9655	0.9647	0.9674	0.9655	0.0000
0.1153	1.93	13	0.0929	0.9655	0.9647	0.9674	0.9655	0.0000
0.2246	2.96	20	0.1026	0.9655	0.9647	0.9674	0.9655	0.0000
0.2246	4.0	27	0.0391	0.9655	0.9647	0.9674	0.9655	0.0000
0.1433	4.89	33	0.0673	0.9655	0.9647	0.9674	0.9655	0.0000
0.1816	5.93	40	0.0794	0.9655	0.9647	0.9674	0.9655	0.0000
0.1816	6.96	47	0.0687	0.9655	0.9647	0.9674	0.9655	0.0000
0.1448	8.0	54	0.1123	0.9655	0.9647	0.9674	0.9655	0.0000
0.1124	8.89	60	0.1855	0.9655	0.9647	0.9674	0.9655	0.0000

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3