SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001

Hasano20/SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001

SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001 is a machine learning model from Hasano20. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

License: other
Model type: segformer
Access: Public
Created: Oct 12, 2024
Updated: Oct 12, 2024
SHA: 4b7b2476

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0217
Mean Iou: 0.9708
Mean Accuracy: 0.9835
Overall Accuracy: 0.9941
Accuracy Background: 0.9965
Accuracy Melt: 0.9584
Accuracy Substrate: 0.9957
Iou Background: 0.9940
Iou Melt: 0.9288
Iou Substrate: 0.9895

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.1107	0.8850	50	0.1152	0.8138	0.8439	0.9627	0.9781	0.5623	0.9914	0.9677	0.5412	0.9325
0.0564	1.7699	100	0.0520	0.9163	0.9432	0.9829	0.9967	0.8488	0.9841	0.9806	0.7963	0.9721
0.0296	2.6549	150	0.0270	0.9557	0.9821	0.9906	0.9916	0.9621	0.9928	0.9893	0.8939	0.9839
0.042	3.5398	200	0.0226	0.9619	0.9763	0.9922	0.9934	0.9384	0.9969	0.9917	0.9077	0.9862
0.0166	4.4248	250	0.0300	0.9616	0.9768	0.9904	0.9957	0.9446	0.9903	0.9872	0.9153	0.9823
0.0159	5.3097	300	0.0203	0.9658	0.9863	0.9931	0.9946	0.9701	0.9941	0.9923	0.9169	0.9883
0.0121	6.1947	350	0.0221	0.9645	0.9795	0.9928	0.9937	0.9480	0.9968	0.9923	0.9141	0.9872
0.0149	7.0796	400	0.0220	0.9648	0.9821	0.9930	0.9949	0.9565	0.9951	0.9930	0.9138	0.9874
0.0352	7.9646	450	0.0215	0.9658	0.9764	0.9933	0.9959	0.9361	0.9971	0.9935	0.9158	0.9880
0.0106	8.8496	500	0.0201	0.9696	0.9820	0.9939	0.9961	0.9535	0.9962	0.9938	0.9256	0.9892
0.0095	9.7345	550	0.0216	0.9674	0.9796	0.9936	0.9955	0.9463	0.9969	0.9936	0.9202	0.9886
0.009	10.6195	600	0.0209	0.9702	0.9821	0.9941	0.9966	0.9539	0.9960	0.9940	0.9273	0.9894
0.0106	11.5044	650	0.0211	0.9700	0.9830	0.9940	0.9964	0.9568	0.9958	0.9940	0.9266	0.9893
0.0099	12.3894	700	0.0217	0.9708	0.9835	0.9941	0.9965	0.9584	0.9957	0.9940	0.9288	0.9895

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Type: segformer
License: other
Created: Oct 12, 2024
Updated: Oct 12, 2024