segformer-b0-miic-tl

This model is a fine-tuned version of nvidia/mit-b0 on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set:

• Loss: 0.3415
• Mean Iou: 0.4569
• Mean Accuracy: 0.9138
• Overall Accuracy: 0.9138
• Accuracy Unlabeled: nan
• Accuracy Circuit: 0.9138
• Iou Unlabeled: 0.0
• Iou Circuit: 0.9138
• Dice Coefficient: 0.8323

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 Unlabeled	Accuracy Circuit	Iou Unlabeled	Iou Circuit	Dice Coefficient
0.3496	3.12	250	0.3203	0.4832	0.9665	0.9665	nan	0.9665	0.0	0.9665	0.8163
0.2808	6.25	500	0.3289	0.4814	0.9629	0.9629	nan	0.9629	0.0	0.9629	0.8271
0.2582	9.38	750	0.3404	0.4670	0.9339	0.9339	nan	0.9339	0.0	0.9339	0.8327
0.2791	12.5	1000	0.3033	0.4591	0.9181	0.9181	nan	0.9181	0.0	0.9181	0.8300
0.2668	15.62	1250	0.3117	0.4559	0.9118	0.9118	nan	0.9118	0.0	0.9118	0.8285
0.2531	18.75	1500	0.2652	0.4686	0.9373	0.9373	nan	0.9373	0.0	0.9373	0.8432
0.2326	21.88	1750	0.3256	0.4604	0.9208	0.9208	nan	0.9208	0.0	0.9208	0.8315
0.2361	25.0	2000	0.3129	0.4656	0.9313	0.9313	nan	0.9313	0.0	0.9313	0.8400
0.2167	28.12	2250	0.3135	0.4558	0.9116	0.9116	nan	0.9116	0.0	0.9116	0.8290
0.2133	31.25	2500	0.3132	0.4560	0.9120	0.9120	nan	0.9120	0.0	0.9120	0.8219
0.1769	34.38	2750	0.3200	0.4441	0.8882	0.8882	nan	0.8882	0.0	0.8882	0.8176
0.1899	37.5	3000	0.3342	0.4612	0.9224	0.9224	nan	0.9224	0.0	0.9224	0.8363
0.1765	40.62	3250	0.3445	0.4625	0.9249	0.9249	nan	0.9249	0.0	0.9249	0.8369
0.1739	43.75	3500	0.3235	0.4608	0.9216	0.9216	nan	0.9216	0.0	0.9216	0.8373
0.1639	46.88	3750	0.3527	0.4591	0.9181	0.9181	nan	0.9181	0.0	0.9181	0.8342
0.1734	50.0	4000	0.3415	0.4569	0.9138	0.9138	nan	0.9138	0.0	0.9138	0.8323

Framework versions

• Transformers 4.36.2
• Pytorch 1.11.0+cu115
• Datasets 2.15.0
• Tokenizers 0.15.0