segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps

This model is a fine-tuned version of nvidia/mit-b0 on the blzncz/4ss1st3r_s3gs3m_24Jan_all dataset. It achieves the following results on the evaluation set:

• Loss: 0.3095
• Mean Iou: 0.5513
• Mean Accuracy: 0.7874
• Overall Accuracy: 0.9260
• Accuracy Bg: nan
• Accuracy Fallo cohesivo: 0.9668
• Accuracy Fallo malla: 0.6808
• Accuracy Fallo adhesivo: 0.9727
• Accuracy Fallo burbuja: 0.5291
• Iou Bg: 0.0
• Iou Fallo cohesivo: 0.9167
• Iou Fallo malla: 0.6189
• Iou Fallo adhesivo: 0.7307
• Iou Fallo burbuja: 0.4903

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: polynomial
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Bg	Accuracy Fallo cohesivo	Accuracy Fallo malla	Accuracy Fallo adhesivo	Accuracy Fallo burbuja	Iou Bg	Iou Fallo cohesivo	Iou Fallo malla	Iou Fallo adhesivo	Iou Fallo burbuja
0.1378	1.0	783	0.2677	0.4895	0.7143	0.9122	nan	0.9724	0.5531	0.9663	0.3654	0.0	0.9038	0.5327	0.6757	0.3351
0.1117	2.0	1566	0.2305	0.5289	0.7978	0.9246	nan	0.9507	0.7727	0.9705	0.4974	0.0	0.9214	0.6808	0.5876	0.4549
0.0881	3.0	2349	0.2041	0.5556	0.7867	0.9354	nan	0.9712	0.7391	0.9389	0.4975	0.0	0.9273	0.6790	0.7323	0.4394
0.0878	4.0	3132	0.1984	0.5584	0.8003	0.9346	nan	0.9556	0.8247	0.9602	0.4606	0.0	0.9261	0.6935	0.7373	0.4352
0.0895	5.0	3915	0.2841	0.5246	0.8086	0.9088	nan	0.9137	0.8834	0.9719	0.4652	0.0	0.8964	0.6309	0.6593	0.4365
0.0773	6.0	4698	0.2547	0.5652	0.7823	0.9336	nan	0.9775	0.6843	0.9384	0.5291	0.0	0.9251	0.6378	0.7820	0.4813
0.0667	7.0	5481	0.2726	0.5609	0.7932	0.9295	nan	0.9741	0.6609	0.9689	0.5689	0.0	0.9203	0.6202	0.7548	0.5093
0.0678	8.0	6264	0.2950	0.5276	0.8002	0.9175	nan	0.9443	0.7561	0.9713	0.5292	0.0	0.9089	0.6570	0.5900	0.4822
0.0653	9.0	7047	0.2712	0.5467	0.7682	0.9288	nan	0.9690	0.6971	0.9641	0.4425	0.0	0.9189	0.6330	0.7588	0.4228
0.0646	10.0	7830	0.2841	0.5499	0.7819	0.9272	nan	0.9681	0.6840	0.9688	0.5068	0.0	0.9178	0.6243	0.7345	0.4728
0.057	11.0	8613	0.3373	0.5257	0.7782	0.9166	nan	0.9593	0.6555	0.9739	0.5242	0.0	0.9075	0.6040	0.6319	0.4848
0.0591	12.0	9396	0.3082	0.5504	0.7900	0.9247	nan	0.9656	0.6776	0.9705	0.5463	0.0	0.9148	0.6172	0.7182	0.5019
0.053	12.77	10000	0.3095	0.5513	0.7874	0.9260	nan	0.9668	0.6808	0.9727	0.5291	0.0	0.9167	0.6189	0.7307	0.4903

Framework versions

• Transformers 4.31.0.dev0
• Pytorch 2.0.1+cpu
• Datasets 2.13.1
• Tokenizers 0.13.3