segformer-b1-finetuned-sudoku

This model is a fine-tuned version of nvidia/mit-b1 on the mrkprc1/SudokuBoundaries2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9826
• Mean Iou: 0.2452
• Mean Accuracy: 0.4999
• Overall Accuracy: 0.4903
• Accuracy Unlabelled: 0.9996
• Accuracy Sudoku-boundary: 0.0001
• Iou Unlabelled: 0.4903
• Iou Sudoku-boundary: 0.0001

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: 6e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabelled	Accuracy Sudoku-boundary	Iou Unlabelled	Iou Sudoku-boundary
0.6034	3.33	20	0.6951	0.3427	0.5173	0.5149	0.6432	0.3914	0.3940	0.2913
0.7796	6.67	40	0.7150	0.3049	0.5083	0.5021	0.8309	0.1857	0.4501	0.1597
0.4378	10.0	60	0.9772	0.2452	0.5	0.4904	1.0	0.0	0.4904	0.0
0.6804	13.33	80	1.1605	0.2452	0.5	0.4904	1.0	0.0	0.4904	0.0
0.58	16.67	100	0.9787	0.2452	0.5	0.4904	1.0	0.0	0.4904	0.0
0.6563	20.0	120	1.1860	0.2452	0.5	0.4904	1.0	0.0	0.4904	0.0
0.5128	23.33	140	0.8884	0.2457	0.5002	0.4907	0.9996	0.0009	0.4905	0.0009
0.5054	26.67	160	0.8746	0.2455	0.5002	0.4907	0.9998	0.0006	0.4905	0.0006
0.5532	30.0	180	0.9540	0.2452	0.5000	0.4905	1.0	0.0000	0.4905	0.0000
0.3238	33.33	200	0.8916	0.2470	0.5009	0.4914	0.9984	0.0035	0.4905	0.0035
0.2964	36.67	220	1.0162	0.2453	0.5000	0.4905	1.0000	0.0000	0.4905	0.0000
0.2102	40.0	240	0.9650	0.2452	0.4998	0.4903	0.9996	0.0001	0.4903	0.0001
0.623	43.33	260	0.9071	0.2461	0.5004	0.4909	0.9991	0.0017	0.4904	0.0017
0.3741	46.67	280	0.9245	0.2454	0.5000	0.4904	0.9994	0.0006	0.4903	0.0006
0.5765	50.0	300	0.9826	0.2452	0.4999	0.4903	0.9996	0.0001	0.4903	0.0001

Framework versions

• Transformers 4.37.1
• Pytorch 2.1.2
• Datasets 2.16.1
• Tokenizers 0.15.1