File size: 12,782 Bytes
1adee0d 0f5de6c 1adee0d 0f5de6c 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d cbd1b79 1adee0d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: model1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model1
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.
It achieves the following results on the evaluation set:
- Loss: 0.2328
- Mean Iou: 0.1042
- Mean Accuracy: 0.1313
- Overall Accuracy: 0.2017
- Accuracy Background: nan
- Accuracy Ship: 0.5956
- Accuracy Small-vehicle: 0.0476
- Accuracy Tennis-court: 0.5923
- Accuracy Helicopter: nan
- Accuracy Basketball-court: 0.0
- Accuracy Ground-track-field: 0.0098
- Accuracy Swimming-pool: 0.0
- Accuracy Harbor: 0.3785
- Accuracy Soccer-ball-field: 0.0
- Accuracy Plane: 0.0
- Accuracy Storage-tank: 0.0
- Accuracy Baseball-diamond: 0.0
- Accuracy Large-vehicle: 0.2151
- Accuracy Bridge: 0.0
- Accuracy Roundabout: 0.0
- Iou Background: 0.0
- Iou Ship: 0.4621
- Iou Small-vehicle: 0.0458
- Iou Tennis-court: 0.5337
- Iou Helicopter: nan
- Iou Basketball-court: 0.0
- Iou Ground-track-field: 0.0097
- Iou Swimming-pool: 0.0
- Iou Harbor: 0.2993
- Iou Soccer-ball-field: 0.0
- Iou Plane: 0.0
- Iou Storage-tank: 0.0
- Iou Baseball-diamond: 0.0
- Iou Large-vehicle: 0.2124
- 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: 1200
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------------:|:----------------------:|:---------------:|:--------------------------:|:--------------:|:---------------------:|:-------------------------:|:----------------------:|:---------------:|:-------------------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------------------:|:----------------------:|:-----------------:|:----------:|:---------------------:|:---------:|:----------------:|:--------------------:|:-----------------:|:----------:|:--------------:|
| 1.9822 | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|