BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB

This model is a fine-tuned version of microsoft/beit-base-finetuned-ade-640-640 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0652
• Mean Iou: 0.9446
• Mean Accuracy: 0.9687
• Overall Accuracy: 0.9855
• Accuracy Background: 0.9893
• Accuracy Melt: 0.9263
• Accuracy Substrate: 0.9905
• Iou Background: 0.9796
• Iou Melt: 0.8757
• Iou Substrate: 0.9785

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 200
• num_epochs: 50

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
0.1893	3.7037	100	0.1358	0.9180	0.9486	0.9815	0.9923	0.8651	0.9884	0.9821	0.7997	0.9723
0.0951	7.4074	200	0.0746	0.9483	0.9791	0.9876	0.9909	0.9575	0.9890	0.9860	0.8775	0.9814
0.0892	11.1111	300	0.0631	0.9489	0.9772	0.9866	0.9875	0.9536	0.9903	0.9817	0.8854	0.9796
0.0878	14.8148	400	0.0692	0.9375	0.9687	0.9829	0.9853	0.9332	0.9877	0.9742	0.8632	0.9750
0.07	18.5185	500	0.0631	0.9419	0.9700	0.9844	0.9865	0.9339	0.9897	0.9777	0.8714	0.9767
0.0507	22.2222	600	0.0646	0.9379	0.9659	0.9829	0.9879	0.9230	0.9869	0.9738	0.8653	0.9747
0.0523	25.9259	700	0.0569	0.9485	0.9724	0.9864	0.9903	0.9371	0.9898	0.9809	0.8855	0.9791
0.0402	29.6296	800	0.0622	0.9422	0.9686	0.9848	0.9881	0.9279	0.9897	0.9777	0.8711	0.9778
0.0406	33.3333	900	0.0631	0.9427	0.9683	0.9852	0.9894	0.9256	0.9899	0.9790	0.8707	0.9785
0.0389	37.0370	1000	0.0650	0.9436	0.9695	0.9852	0.9879	0.9303	0.9904	0.9784	0.8739	0.9784
0.0396	40.7407	1100	0.0634	0.9457	0.9700	0.9857	0.9900	0.9301	0.9899	0.9798	0.8787	0.9787
0.036	44.4444	1200	0.0653	0.9443	0.9684	0.9855	0.9894	0.9254	0.9906	0.9797	0.8748	0.9786
0.0224	48.1481	1300	0.0652	0.9446	0.9687	0.9855	0.9893	0.9263	0.9905	0.9796	0.8757	0.9785

Framework versions

• Transformers 4.41.2
• Pytorch 2.0.1+cu117
• Datasets 2.19.2
• Tokenizers 0.19.1