model2

This model is a fine-tuned version of nvidia/mit-b2 on the giuseppemartino/isaid_sam_predicted dataset. It achieves the following results on the evaluation set:

• Loss: 0.2318
• Mean Iou: 0.2504
• Mean Accuracy: 0.3019
• Overall Accuracy: 0.4542
• Accuracy Background: nan
• Accuracy Ship: 0.6330
• Accuracy Small-vehicle: 0.4644
• Accuracy Tennis-court: 0.0280
• Accuracy Helicopter: nan
• Accuracy Basketball-court: 0.0
• Accuracy Ground-track-field: 0.6010
• Accuracy Swimming-pool: nan
• Accuracy Harbor: 0.4575
• Accuracy Soccer-ball-field: 0.7776
• Accuracy Plane: nan
• Accuracy Storage-tank: 0.0
• Accuracy Baseball-diamond: nan
• Accuracy Large-vehicle: 0.3594
• Accuracy Bridge: 0.0
• Accuracy Roundabout: 0.0
• Iou Background: 0.0
• Iou Ship: 0.5194
• Iou Small-vehicle: 0.4368
• Iou Tennis-court: 0.0280
• Iou Helicopter: nan
• Iou Basketball-court: 0.0
• Iou Ground-track-field: 0.5492
• Iou Swimming-pool: nan
• Iou Harbor: 0.3611
• Iou Soccer-ball-field: 0.7592
• Iou Plane: nan
• Iou Storage-tank: 0.0
• Iou Baseball-diamond: nan
• Iou Large-vehicle: 0.3508
• Iou Bridge: 0.0
• Iou Roundabout: 0.0

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: 1345

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Ship	Accuracy Small-vehicle	Accuracy Tennis-court	Accuracy Helicopter	Accuracy Basketball-court	Accuracy Ground-track-field	Accuracy Swimming-pool	Accuracy Harbor	Accuracy Soccer-ball-field	Accuracy Plane	Accuracy Storage-tank	Accuracy Baseball-diamond	Accuracy Large-vehicle	Accuracy Bridge	Accuracy Roundabout	Iou Background	Iou Ship	Iou Small-vehicle	Iou Tennis-court	Iou Helicopter	Iou Basketball-court	Iou Ground-track-field	Iou Swimming-pool	Iou Harbor	Iou Soccer-ball-field	Iou Plane	Iou Storage-tank	Iou Baseball-diamond	Iou Large-vehicle	Iou Bridge	Iou Roundabout
1.1413	1.0	113	0.5054	0.0431	0.0841	0.0445	nan	0.0611	0.0179	0.0079	nan	0.0	0.0	nan	0.8374	0.0	nan	0.0	nan	0.0010	0.0	0.0	0.0	0.0592	0.0178	0.0079	nan	0.0	0.0	nan	0.4319	0.0	nan	0.0	nan	0.0010	0.0	0.0
0.326	2.0	226	0.3240	0.0756	0.1192	0.2144	nan	0.0433	0.1690	0.0	nan	0.0	0.0	nan	0.8062	0.0	nan	0.0	nan	0.2926	0.0	0.0	0.0	0.0430	0.1614	0.0	nan	0.0	0.0	nan	0.4129	0.0	nan	0.0	nan	0.2904	0.0	0.0
0.1849	3.0	339	0.2807	0.1589	0.2164	0.3238	nan	0.3520	0.3125	0.0	nan	0.0	0.3563	nan	0.6252	0.4509	nan	0.0	nan	0.2835	0.0	0.0	0.0	0.3236	0.2894	0.0	nan	0.0	0.3265	0.0	0.3954	0.4506	nan	0.0	nan	0.2807	0.0	0.0
0.1341	4.0	452	0.2694	0.1618	0.2309	0.3055	nan	0.2089	0.3628	0.0188	nan	0.0	0.4866	nan	0.7552	0.5206	nan	0.0	nan	0.1866	0.0	0.0	0.0	0.2004	0.3303	0.0188	nan	0.0	0.4268	0.0	0.4221	0.5205	nan	0.0	nan	0.1840	0.0	0.0
0.1282	5.0	565	0.2631	0.2057	0.2726	0.3396	nan	0.4061	0.3347	0.0292	nan	0.0	0.6126	nan	0.6152	0.8252	nan	0.0	nan	0.1751	0.0	0.0	0.0	0.3667	0.3169	0.0292	nan	0.0	0.4767	nan	0.3995	0.7049	nan	0.0	nan	0.1745	0.0	0.0
0.1138	6.0	678	0.2418	0.1949	0.2558	0.3865	nan	0.2362	0.3709	0.0122	nan	0.0	0.6128	nan	0.6627	0.5823	nan	0.0	nan	0.3365	0.0	0.0	0.0	0.2249	0.3444	0.0122	nan	0.0	0.4625	nan	0.3921	0.5725	nan	0.0	nan	0.3301	0.0	0.0
0.1049	7.0	791	0.2345	0.2013	0.2623	0.4725	nan	0.3186	0.4071	0.0827	nan	0.0	0.1697	nan	0.7809	0.6140	nan	0.0	nan	0.5118	0.0	0.0	0.0	0.2927	0.3851	0.0827	nan	0.0	0.1679	nan	0.4702	0.5212	nan	0.0	nan	0.4961	0.0	0.0
0.0829	8.0	904	0.2351	0.2194	0.2818	0.4348	nan	0.1689	0.4289	0.0980	nan	0.0	0.5547	nan	0.7050	0.7860	nan	0.0	nan	0.3580	0.0	0.0	0.0	0.1619	0.4048	0.0980	nan	0.0	0.5205	nan	0.3967	0.7023	nan	0.0	nan	0.3490	0.0	0.0
0.0922	9.0	1017	0.2350	0.2549	0.3103	0.5060	nan	0.4729	0.4726	0.0572	nan	0.0	0.5679	nan	0.5794	0.7942	nan	0.0	nan	0.4690	0.0	0.0	0.0	0.4143	0.4398	0.0572	nan	0.0	0.5293	nan	0.4010	0.7613	nan	0.0	nan	0.4563	0.0	0.0
0.0717	10.0	1130	0.2399	0.2344	0.2871	0.4150	nan	0.4512	0.4155	0.0169	nan	0.0	0.5706	nan	0.6279	0.7676	nan	0.0	nan	0.3089	0.0	0.0	0.0	0.3995	0.3949	0.0169	nan	0.0	0.5351	nan	0.4246	0.7393	nan	0.0	nan	0.3023	0.0	0.0
0.0787	11.0	1243	0.2228	0.2578	0.3105	0.4726	nan	0.6679	0.4378	0.0666	nan	0.0	0.5865	nan	0.4684	0.7796	nan	0.0	nan	0.4087	0.0	0.0	0.0	0.5359	0.4172	0.0666	nan	0.0	0.5456	nan	0.3785	0.7528	nan	0.0	nan	0.3975	0.0	0.0
0.0787	11.9	1345	0.2318	0.2504	0.3019	0.4542	nan	0.6330	0.4644	0.0280	nan	0.0	0.6010	nan	0.4575	0.7776	nan	0.0	nan	0.3594	0.0	0.0	0.0	0.5194	0.4368	0.0280	nan	0.0	0.5492	nan	0.3611	0.7592	nan	0.0	nan	0.3508	0.0	0.0

Framework versions

• Transformers 4.35.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1