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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: model1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# model1 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the giuseppemartino/i-SAID_custom_or_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2328 |
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- Mean Iou: 0.1042 |
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- Mean Accuracy: 0.1313 |
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- Overall Accuracy: 0.2017 |
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- Accuracy Background: nan |
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- Accuracy Ship: 0.5956 |
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- Accuracy Small-vehicle: 0.0476 |
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- Accuracy Tennis-court: 0.5923 |
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- Accuracy Helicopter: nan |
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- Accuracy Basketball-court: 0.0 |
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- Accuracy Ground-track-field: 0.0098 |
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- Accuracy Swimming-pool: 0.0 |
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- Accuracy Harbor: 0.3785 |
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- Accuracy Soccer-ball-field: 0.0 |
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- Accuracy Plane: 0.0 |
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- Accuracy Storage-tank: 0.0 |
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- Accuracy Baseball-diamond: 0.0 |
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- Accuracy Large-vehicle: 0.2151 |
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- Accuracy Bridge: 0.0 |
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- Accuracy Roundabout: 0.0 |
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- Iou Background: 0.0 |
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- Iou Ship: 0.4621 |
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- Iou Small-vehicle: 0.0458 |
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- Iou Tennis-court: 0.5337 |
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- Iou Helicopter: nan |
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- Iou Basketball-court: 0.0 |
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- Iou Ground-track-field: 0.0097 |
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- Iou Swimming-pool: 0.0 |
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- Iou Harbor: 0.2993 |
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- Iou Soccer-ball-field: 0.0 |
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- Iou Plane: 0.0 |
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- Iou Storage-tank: 0.0 |
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- Iou Baseball-diamond: 0.0 |
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- Iou Large-vehicle: 0.2124 |
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- Iou Bridge: 0.0 |
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- Iou Roundabout: 0.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- training_steps: 1200 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:| |
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| 1.9822 | 1.0 | 105 | 1.2892 | 0.0989 | 0.1440 | 0.2348 | nan | 0.4735 | 0.0 | 0.8169 | nan | 0.0 | 0.0 | 0.0 | 0.4963 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2296 | 0.0 | 0.0 | 0.0 | 0.2526 | 0.0 | 0.6683 | nan | 0.0 | 0.0 | 0.0 | 0.3355 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2269 | 0.0 | 0.0 | |
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| 1.2543 | 2.0 | 210 | 0.8623 | 0.0866 | 0.1170 | 0.2348 | nan | 0.1505 | 0.0 | 0.8538 | nan | 0.0 | 0.0 | 0.0 | 0.4055 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2275 | 0.0 | 0.0 | 0.0 | 0.0861 | 0.0 | 0.7363 | nan | 0.0 | 0.0 | 0.0 | 0.2519 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2248 | 0.0 | 0.0 | |
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| 0.8713 | 3.0 | 315 | 0.5622 | 0.0639 | 0.0761 | 0.1772 | nan | 0.0095 | 0.0 | 0.5983 | nan | 0.0 | 0.0 | 0.0 | 0.2609 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1963 | 0.0 | 0.0 | 0.0 | 0.0091 | 0.0 | 0.5714 | nan | 0.0 | 0.0 | 0.0 | 0.1821 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1953 | 0.0 | 0.0 | |
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| 0.5934 | 4.0 | 420 | 0.4178 | 0.0698 | 0.0859 | 0.2062 | nan | 0.0156 | 0.0 | 0.5852 | nan | 0.0 | 0.0 | 0.0 | 0.3260 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2754 | 0.0 | 0.0 | 0.0 | 0.0137 | 0.0 | 0.5481 | nan | 0.0 | 0.0 | 0.0 | 0.2149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2706 | 0.0 | 0.0 | |
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| 0.4793 | 5.0 | 525 | 0.3240 | 0.0518 | 0.0630 | 0.1120 | nan | 0.1356 | 0.0005 | 0.4301 | nan | 0.0 | 0.0 | 0.0 | 0.2204 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0954 | 0.0 | 0.0 | 0.0 | 0.1177 | 0.0005 | 0.3972 | nan | 0.0 | 0.0 | 0.0 | 0.1673 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0951 | 0.0 | 0.0 | |
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| 0.3711 | 6.0 | 630 | 0.2836 | 0.0736 | 0.0930 | 0.1310 | nan | 0.4607 | 0.0002 | 0.5083 | nan | 0.0 | 0.0000 | 0.0 | 0.2322 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1002 | 0.0 | 0.0 | 0.0 | 0.3787 | 0.0002 | 0.4270 | nan | 0.0 | 0.0000 | 0.0 | 0.1978 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0998 | 0.0 | 0.0 | |
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| 0.347 | 7.0 | 735 | 0.2647 | 0.0988 | 0.1242 | 0.1963 | nan | 0.5288 | 0.0160 | 0.5769 | nan | 0.0 | 0.0001 | 0.0 | 0.3912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2261 | 0.0 | 0.0 | 0.0 | 0.4020 | 0.0159 | 0.5461 | nan | 0.0 | 0.0001 | 0.0 | 0.2955 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2223 | 0.0 | 0.0 | |
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| 0.3004 | 8.0 | 840 | 0.2667 | 0.1135 | 0.1445 | 0.2693 | nan | 0.5257 | 0.0617 | 0.6456 | nan | 0.0 | 0.0006 | 0.0 | 0.4247 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3640 | 0.0 | 0.0 | 0.0 | 0.4010 | 0.0590 | 0.5757 | nan | 0.0 | 0.0006 | 0.0 | 0.3104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3557 | 0.0 | 0.0 | |
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| 0.2622 | 9.0 | 945 | 0.2399 | 0.0856 | 0.1053 | 0.1591 | nan | 0.4918 | 0.0207 | 0.5720 | nan | 0.0 | 0.0001 | 0.0 | 0.2555 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1344 | 0.0 | 0.0 | 0.0 | 0.4010 | 0.0203 | 0.5078 | nan | 0.0 | 0.0001 | 0.0 | 0.2207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1334 | 0.0 | 0.0 | |
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| 0.2489 | 10.0 | 1050 | 0.2446 | 0.1002 | 0.1257 | 0.1846 | nan | 0.5400 | 0.0391 | 0.5641 | nan | 0.0 | 0.0030 | 0.0 | 0.4262 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1880 | 0.0 | 0.0 | 0.0 | 0.4294 | 0.0379 | 0.5256 | nan | 0.0 | 0.0030 | 0.0 | 0.3212 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1860 | 0.0 | 0.0 | |
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| 0.242 | 11.0 | 1155 | 0.2346 | 0.0957 | 0.1198 | 0.1773 | nan | 0.5657 | 0.0261 | 0.5443 | nan | 0.0 | 0.0024 | 0.0 | 0.3529 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1854 | 0.0 | 0.0 | 0.0 | 0.4501 | 0.0257 | 0.4917 | nan | 0.0 | 0.0024 | 0.0 | 0.2829 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1834 | 0.0 | 0.0 | |
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| 0.2276 | 11.43 | 1200 | 0.2328 | 0.1042 | 0.1313 | 0.2017 | nan | 0.5956 | 0.0476 | 0.5923 | nan | 0.0 | 0.0098 | 0.0 | 0.3785 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2151 | 0.0 | 0.0 | 0.0 | 0.4621 | 0.0458 | 0.5337 | nan | 0.0 | 0.0097 | 0.0 | 0.2993 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2124 | 0.0 | 0.0 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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