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giuseppemartino
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model1

Image Segmentation
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PyTorch
segformer
vision
Generated from Trainer
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  1. README.md +35 -41
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@@ -1,9 +1,7 @@
1
  ---
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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
@@ -15,42 +13,42 @@ 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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@@ -77,24 +75,20 @@ The following hyperparameters were used during training:
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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
 
1
  ---
2
  license: other
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+ base_model: nvidia/mit-b2
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  tags:
 
 
5
  - generated_from_trainer
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  model-index:
7
  - name: model1
 
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14
  # model1
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+ This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1646
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+ - Mean Iou: 0.2689
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+ - Mean Accuracy: 0.3089
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+ - Overall Accuracy: 0.3928
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  - Accuracy Background: nan
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+ - Accuracy Ship: 0.7889
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+ - Accuracy Small-vehicle: 0.3939
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+ - Accuracy Tennis-court: 0.6399
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  - Accuracy Helicopter: nan
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  - Accuracy Basketball-court: 0.0
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+ - Accuracy Ground-track-field: 0.4337
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+ - Accuracy Swimming-pool: 0.6049
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+ - Accuracy Harbor: 0.3386
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+ - Accuracy Soccer-ball-field: 0.2551
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+ - Accuracy Plane: 0.0001
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  - Accuracy Storage-tank: 0.0
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+ - Accuracy Baseball-diamond: 0.5217
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+ - Accuracy Large-vehicle: 0.3477
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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: 0.6137
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+ - Iou Small-vehicle: 0.3354
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+ - Iou Tennis-court: 0.6399
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  - Iou Helicopter: nan
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  - Iou Basketball-court: 0.0
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+ - Iou Ground-track-field: 0.4084
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+ - Iou Swimming-pool: 0.6049
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+ - Iou Harbor: 0.3165
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+ - Iou Soccer-ball-field: 0.2514
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+ - Iou Plane: 0.0001
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  - Iou Storage-tank: 0.0
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+ - Iou Baseball-diamond: 0.5217
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+ - Iou Large-vehicle: 0.3418
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  - Iou Bridge: 0.0
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  - Iou Roundabout: 0.0
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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: 840
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  ### Training results
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82
  | 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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+ | 1.1466 | 1.0 | 105 | 0.3419 | 0.0260 | 0.0279 | 0.0687 | nan | 0.0068 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0036 | 0.3562 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0240 | 0.0 | 0.0 |
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+ | 0.3289 | 2.0 | 210 | 0.2301 | 0.1252 | 0.1441 | 0.2674 | nan | 0.5316 | 0.1793 | 0.6775 | nan | 0.0 | 0.0324 | 0.1854 | 0.1185 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2923 | 0.0 | 0.0 | 0.0 | 0.4189 | 0.1612 | 0.6752 | nan | 0.0 | 0.0321 | 0.1854 | 0.1157 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2898 | 0.0 | 0.0 |
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+ | 0.1819 | 3.0 | 315 | 0.1965 | 0.1611 | 0.1937 | 0.3286 | nan | 0.7305 | 0.2842 | 0.4229 | nan | 0.0 | 0.3566 | 0.2424 | 0.1707 | 0.0739 | 0.0 | 0.0 | 0.0 | 0.4300 | 0.0 | 0.0 | 0.0 | 0.5605 | 0.2492 | 0.4229 | nan | 0.0 | 0.2817 | 0.2424 | 0.1637 | 0.0738 | 0.0 | 0.0 | 0.0 | 0.4223 | 0.0 | 0.0 |
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+ | 0.1505 | 4.0 | 420 | 0.1760 | 0.1987 | 0.2352 | 0.3689 | nan | 0.7552 | 0.3079 | 0.5796 | nan | 0.0 | 0.4515 | 0.4367 | 0.2065 | 0.1437 | 0.0 | 0.0 | 0.0 | 0.4115 | 0.0 | 0.0 | 0.0 | 0.5715 | 0.2762 | 0.5790 | nan | 0.0 | 0.3752 | 0.4367 | 0.1957 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.4029 | 0.0 | 0.0 |
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+ | 0.1269 | 5.0 | 525 | 0.1688 | 0.2239 | 0.2616 | 0.3561 | nan | 0.8249 | 0.3133 | 0.5309 | nan | 0.0 | 0.3966 | 0.6398 | 0.2513 | 0.1975 | 0.0003 | 0.0 | 0.1336 | 0.3738 | 0.0 | 0.0 | 0.0 | 0.6006 | 0.2833 | 0.5309 | nan | 0.0 | 0.3711 | 0.6398 | 0.2378 | 0.1957 | 0.0003 | 0.0 | 0.1336 | 0.3661 | 0.0 | 0.0 |
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+ | 0.1012 | 6.0 | 630 | 0.1763 | 0.2563 | 0.3036 | 0.3830 | nan | 0.7977 | 0.4801 | 0.6774 | nan | 0.0 | 0.4913 | 0.7772 | 0.2993 | 0.2702 | 0.0 | 0.0 | 0.2024 | 0.2541 | 0.0 | 0.0 | 0.0 | 0.6060 | 0.3488 | 0.6774 | nan | 0.0 | 0.4359 | 0.7767 | 0.2816 | 0.2638 | 0.0 | 0.0 | 0.2024 | 0.2515 | 0.0 | 0.0 |
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+ | 0.0996 | 7.0 | 735 | 0.1687 | 0.2515 | 0.2906 | 0.3644 | nan | 0.7947 | 0.3775 | 0.5884 | nan | 0.0 | 0.4452 | 0.5756 | 0.2734 | 0.2140 | 0.0 | 0.0 | 0.4769 | 0.3225 | 0.0 | 0.0 | 0.0 | 0.6093 | 0.3246 | 0.5884 | nan | 0.0 | 0.4081 | 0.5756 | 0.2599 | 0.2128 | 0.0 | 0.0 | 0.4769 | 0.3174 | 0.0 | 0.0 |
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+ | 0.0945 | 8.0 | 840 | 0.1646 | 0.2689 | 0.3089 | 0.3928 | nan | 0.7889 | 0.3939 | 0.6399 | nan | 0.0 | 0.4337 | 0.6049 | 0.3386 | 0.2551 | 0.0001 | 0.0 | 0.5217 | 0.3477 | 0.0 | 0.0 | 0.0 | 0.6137 | 0.3354 | 0.6399 | nan | 0.0 | 0.4084 | 0.6049 | 0.3165 | 0.2514 | 0.0001 | 0.0 | 0.5217 | 0.3418 | 0.0 | 0.0 |
 
 
 
 
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  ### Framework versions