End of training

7eefd23 verified 2 months ago

6.7 kB

metadata

library_name: transformers
license: other
base_model: >-
  NICOPOI-9/segformer-b5-finetuned-ade20k-morphpadver1-hgo-coord-v9_mix_resample_40epochs
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: >-
      segformer-b5-finetuned-morphpad-morphpadver1-hgo-coord-v9_16mat_2void_40epochs
    results: []

segformer-b5-finetuned-morphpad-morphpadver1-hgo-coord-v9_16mat_2void_40epochs

This model is a fine-tuned version of NICOPOI-9/segformer-b5-finetuned-ade20k-morphpadver1-hgo-coord-v9_mix_resample_40epochs on the NICOPOI-9/morphpad_16_labels_2voids_gaussian dataset. It achieves the following results on the evaluation set:

Loss: 0.3575
Mean Iou: 0.8930
Mean Accuracy: 0.9416
Overall Accuracy: 0.9463
Accuracy [0,0]: 0.9359
Accuracy [0,1]: 0.9388
Accuracy [1,0]: 0.9489
Accuracy [1,1]: 0.9515
Accuracy [0,2]: 0.9458
Accuracy [0,3]: 0.9591
Accuracy [1,2]: 0.9443
Accuracy [1,3]: 0.9617
Accuracy [2,0]: 0.9116
Accuracy [2,1]: 0.9338
Accuracy [2,2]: 0.9560
Accuracy [2,3]: 0.9303
Accuracy [3,0]: 0.9475
Accuracy [3,1]: 0.9086
Accuracy [3,2]: 0.9391
Accuracy [3,3]: 0.9160
Accuracy Void: 0.9788
Iou [0,0]: 0.8957
Iou [0,1]: 0.9112
Iou [1,0]: 0.8826
Iou [1,1]: 0.9078
Iou [0,2]: 0.8907
Iou [0,3]: 0.9174
Iou [1,2]: 0.8766
Iou [1,3]: 0.8767
Iou [2,0]: 0.8790
Iou [2,1]: 0.9008
Iou [2,2]: 0.8806
Iou [2,3]: 0.8972
Iou [3,0]: 0.9088
Iou [3,1]: 0.8467
Iou [3,2]: 0.8877
Iou [3,3]: 0.8755
Iou Void: 0.9455

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: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy [0,0]	Accuracy [0,1]	Accuracy [1,0]	Accuracy [1,1]	Accuracy [0,2]	Accuracy [0,3]	Accuracy [1,2]	Accuracy [1,3]	Accuracy [2,0]	Accuracy [2,1]	Accuracy [2,2]	Accuracy [2,3]	Accuracy [3,0]	Accuracy [3,1]	Accuracy [3,2]	Accuracy [3,3]	Accuracy Void	Iou [0,0]	Iou [0,1]	Iou [1,0]	Iou [1,1]	Iou [0,2]	Iou [0,3]	Iou [1,2]	Iou [1,3]	Iou [2,0]	Iou [2,1]	Iou [2,2]	Iou [2,3]	Iou [3,0]	Iou [3,1]	Iou [3,2]	Iou [3,3]	Iou Void
0.2611	7.3260	4000	0.4402	0.7621	0.8618	0.8738	0.8911	0.8941	0.9373	0.8466	0.8823	0.8020	0.8669	0.8736	0.8264	0.8524	0.8802	0.8070	0.8686	0.7729	0.8628	0.8363	0.9506	0.7294	0.8149	0.7502	0.7804	0.7516	0.7345	0.7654	0.7698	0.7054	0.7860	0.7727	0.7222	0.7650	0.7060	0.7560	0.7201	0.9259
0.0752	14.6520	8000	0.3003	0.8459	0.9150	0.9219	0.9089	0.9053	0.9341	0.9189	0.9172	0.9234	0.8981	0.9248	0.9091	0.8928	0.9286	0.9263	0.9165	0.8866	0.9078	0.8821	0.9748	0.8681	0.8589	0.8396	0.8530	0.8516	0.8442	0.8280	0.8354	0.8609	0.8350	0.8572	0.8455	0.8423	0.7927	0.8292	0.8006	0.9391
0.0345	21.9780	12000	0.3628	0.8687	0.9270	0.9333	0.9331	0.9187	0.9463	0.9345	0.9437	0.9366	0.9222	0.9471	0.9114	0.9126	0.9537	0.9311	0.9123	0.8793	0.9177	0.8809	0.9784	0.8684	0.8907	0.8609	0.8826	0.8744	0.8767	0.8494	0.8459	0.8656	0.8727	0.8758	0.8759	0.8715	0.8379	0.8433	0.8352	0.9405
0.0422	29.3040	16000	0.3604	0.8848	0.9367	0.9422	0.9402	0.9259	0.9539	0.9297	0.9421	0.9427	0.9482	0.9681	0.8912	0.9222	0.9529	0.9482	0.9337	0.8971	0.9419	0.9112	0.9746	0.8744	0.9078	0.8724	0.8885	0.8864	0.8982	0.8794	0.8767	0.8503	0.8826	0.8739	0.9045	0.8934	0.8480	0.8898	0.8710	0.9449
0.0304	36.6300	20000	0.3575	0.8930	0.9416	0.9463	0.9359	0.9388	0.9489	0.9515	0.9458	0.9591	0.9443	0.9617	0.9116	0.9338	0.9560	0.9303	0.9475	0.9086	0.9391	0.9160	0.9788	0.8957	0.9112	0.8826	0.9078	0.8907	0.9174	0.8766	0.8767	0.8790	0.9008	0.8806	0.8972	0.9088	0.8467	0.8877	0.8755	0.9455

Framework versions

Transformers 4.48.3
Pytorch 2.1.0
Datasets 3.2.0
Tokenizers 0.21.0