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PushkarA07/
segformer-b0-finetuned-net-4Sep

Name: segformer-b0-finetuned-net-4Sep
Availability: InStock

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PushkarA07/segformer-b0-finetuned-net-4Sep

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segformer-b0-finetuned-net-4Sep

This model is a fine-tuned version of PushkarA07/segformer-b0-finetuned-net-4Sep on the PushkarA07/batch2-tiles_W5 dataset. It achieves the following results on the evaluation set:

Loss: 0.0035
Mean Iou: 0.8702
Mean Accuracy: 0.9096
Overall Accuracy: 0.9987
Accuracy Abnormality: 0.8196
Iou Abnormality: 0.7417

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: 1e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Abnormality	Iou Abnormality
0.0025	0.4348	10	0.0035	0.8709	0.9118	0.9987	0.8241	0.7432
0.0044	0.8696	20	0.0035	0.8703	0.9105	0.9987	0.8215	0.7420
0.0128	1.3043	30	0.0035	0.8701	0.9090	0.9987	0.8185	0.7415
0.0056	1.7391	40	0.0035	0.8700	0.9087	0.9987	0.8180	0.7413
0.0083	2.1739	50	0.0035	0.8702	0.9100	0.9987	0.8205	0.7418
0.0051	2.6087	60	0.0035	0.8703	0.9101	0.9987	0.8207	0.7420
0.0075	3.0435	70	0.0036	0.8704	0.9111	0.9987	0.8227	0.7422
0.0078	3.4783	80	0.0035	0.8704	0.9105	0.9987	0.8214	0.7421
0.0033	3.9130	90	0.0035	0.8706	0.9118	0.9987	0.8241	0.7426
0.0062	4.3478	100	0.0035	0.8702	0.9111	0.9987	0.8227	0.7418
0.003	4.7826	110	0.0036	0.8699	0.9088	0.9987	0.8181	0.7412
0.0114	5.2174	120	0.0035	0.8699	0.9087	0.9987	0.8178	0.7412
0.014	5.6522	130	0.0036	0.8699	0.9086	0.9987	0.8177	0.7412
0.0118	6.0870	140	0.0035	0.8700	0.9086	0.9987	0.8176	0.7413
0.0058	6.5217	150	0.0036	0.8701	0.9098	0.9987	0.8201	0.7416
0.0109	6.9565	160	0.0035	0.8701	0.9096	0.9987	0.8197	0.7416
0.0044	7.3913	170	0.0036	0.8708	0.9126	0.9987	0.8257	0.7429
0.0036	7.8261	180	0.0035	0.8704	0.9104	0.9987	0.8213	0.7422
0.0143	8.2609	190	0.0035	0.8701	0.9089	0.9987	0.8183	0.7415
0.0083	8.6957	200	0.0035	0.8705	0.9110	0.9987	0.8224	0.7424
0.0039	9.1304	210	0.0035	0.8704	0.9108	0.9987	0.8220	0.7421
0.0053	9.5652	220	0.0036	0.8704	0.9108	0.9987	0.8222	0.7421
0.0045	10.0	230	0.0035	0.8702	0.9096	0.9987	0.8196	0.7417

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.2.0
Tokenizers 0.22.1