File size: 9,223 Bytes
adba754 4f5695e adba754 4f5695e adba754 |
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 |
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGB-b0_3
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. -->
# dropoff-utcustom-train-SF-RGB-b0_3
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3958
- Mean Iou: 0.6134
- Mean Accuracy: 0.6480
- Overall Accuracy: 0.9627
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.3026
- Accuracy Undropoff: 0.9933
- Iou Unlabeled: nan
- Iou Dropoff: 0.2645
- Iou Undropoff: 0.9622
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.1015 | 3.33 | 10 | 1.0990 | 0.1184 | 0.4572 | 0.3294 | nan | 0.5975 | 0.3170 | 0.0 | 0.0427 | 0.3124 |
| 1.0478 | 6.67 | 20 | 1.0756 | 0.2121 | 0.7082 | 0.5654 | nan | 0.8648 | 0.5515 | 0.0 | 0.0879 | 0.5482 |
| 1.0451 | 10.0 | 30 | 1.0269 | 0.2846 | 0.8053 | 0.7334 | nan | 0.8842 | 0.7264 | 0.0 | 0.1313 | 0.7226 |
| 0.9095 | 13.33 | 40 | 0.9476 | 0.3360 | 0.7905 | 0.8411 | nan | 0.7349 | 0.8460 | 0.0 | 0.1723 | 0.8358 |
| 0.8091 | 16.67 | 50 | 0.8425 | 0.3858 | 0.7645 | 0.9167 | nan | 0.5975 | 0.9315 | 0.0 | 0.2429 | 0.9145 |
| 0.8094 | 20.0 | 60 | 0.7489 | 0.4090 | 0.7445 | 0.9417 | nan | 0.5281 | 0.9608 | 0.0 | 0.2866 | 0.9403 |
| 0.6945 | 23.33 | 70 | 0.7005 | 0.4148 | 0.7472 | 0.9453 | nan | 0.5298 | 0.9646 | 0.0 | 0.3004 | 0.9440 |
| 0.6337 | 26.67 | 80 | 0.6331 | 0.6267 | 0.7334 | 0.9499 | nan | 0.4958 | 0.9709 | nan | 0.3047 | 0.9488 |
| 0.603 | 30.0 | 90 | 0.5726 | 0.6222 | 0.6935 | 0.9559 | nan | 0.4057 | 0.9814 | nan | 0.2894 | 0.9551 |
| 0.5903 | 33.33 | 100 | 0.5841 | 0.6248 | 0.7151 | 0.9526 | nan | 0.4546 | 0.9757 | nan | 0.2980 | 0.9516 |
| 0.5514 | 36.67 | 110 | 0.5157 | 0.6227 | 0.6818 | 0.9585 | nan | 0.3781 | 0.9854 | nan | 0.2875 | 0.9578 |
| 0.6464 | 40.0 | 120 | 0.5141 | 0.6240 | 0.6889 | 0.9575 | nan | 0.3941 | 0.9836 | nan | 0.2912 | 0.9568 |
| 0.5198 | 43.33 | 130 | 0.4890 | 0.4141 | 0.6762 | 0.9591 | nan | 0.3657 | 0.9866 | 0.0 | 0.2838 | 0.9585 |
| 0.5077 | 46.67 | 140 | 0.4855 | 0.4118 | 0.6719 | 0.9588 | nan | 0.3572 | 0.9866 | 0.0 | 0.2773 | 0.9581 |
| 0.4817 | 50.0 | 150 | 0.4710 | 0.6182 | 0.6733 | 0.9587 | nan | 0.3602 | 0.9864 | nan | 0.2784 | 0.9580 |
| 0.4713 | 53.33 | 160 | 0.4669 | 0.6196 | 0.6683 | 0.9603 | nan | 0.3479 | 0.9887 | nan | 0.2795 | 0.9597 |
| 0.4516 | 56.67 | 170 | 0.4486 | 0.4107 | 0.6586 | 0.9612 | nan | 0.3265 | 0.9906 | 0.0 | 0.2715 | 0.9606 |
| 0.4059 | 60.0 | 180 | 0.4361 | 0.6136 | 0.6548 | 0.9612 | nan | 0.3187 | 0.9909 | nan | 0.2665 | 0.9606 |
| 0.4142 | 63.33 | 190 | 0.4267 | 0.6115 | 0.6503 | 0.9615 | nan | 0.3089 | 0.9917 | nan | 0.2621 | 0.9610 |
| 0.4393 | 66.67 | 200 | 0.4188 | 0.6035 | 0.6354 | 0.9623 | nan | 0.2768 | 0.9940 | nan | 0.2452 | 0.9618 |
| 0.4071 | 70.0 | 210 | 0.4224 | 0.6137 | 0.6528 | 0.9617 | nan | 0.3138 | 0.9917 | nan | 0.2663 | 0.9612 |
| 0.4009 | 73.33 | 220 | 0.4205 | 0.6136 | 0.6540 | 0.9614 | nan | 0.3167 | 0.9912 | nan | 0.2664 | 0.9608 |
| 0.4043 | 76.67 | 230 | 0.4148 | 0.6132 | 0.6514 | 0.9619 | nan | 0.3108 | 0.9920 | nan | 0.2651 | 0.9613 |
| 0.6302 | 80.0 | 240 | 0.4116 | 0.6133 | 0.6513 | 0.9619 | nan | 0.3105 | 0.9921 | nan | 0.2653 | 0.9614 |
| 0.3859 | 83.33 | 250 | 0.4113 | 0.6141 | 0.6543 | 0.9615 | nan | 0.3174 | 0.9913 | nan | 0.2673 | 0.9609 |
| 0.3791 | 86.67 | 260 | 0.4033 | 0.6042 | 0.6361 | 0.9623 | nan | 0.2782 | 0.9940 | nan | 0.2465 | 0.9619 |
| 0.5716 | 90.0 | 270 | 0.4088 | 0.6168 | 0.6575 | 0.9617 | nan | 0.3237 | 0.9913 | nan | 0.2724 | 0.9612 |
| 0.3803 | 93.33 | 280 | 0.4024 | 0.6171 | 0.6565 | 0.9621 | nan | 0.3211 | 0.9918 | nan | 0.2727 | 0.9615 |
| 0.371 | 96.67 | 290 | 0.3979 | 0.6166 | 0.6539 | 0.9625 | nan | 0.3154 | 0.9925 | nan | 0.2713 | 0.9620 |
| 0.3656 | 100.0 | 300 | 0.3992 | 0.6204 | 0.6615 | 0.9621 | nan | 0.3316 | 0.9913 | nan | 0.2793 | 0.9615 |
| 0.3674 | 103.33 | 310 | 0.3930 | 0.6110 | 0.6433 | 0.9630 | nan | 0.2925 | 0.9941 | nan | 0.2594 | 0.9625 |
| 0.378 | 106.67 | 320 | 0.3925 | 0.6124 | 0.6459 | 0.9629 | nan | 0.2981 | 0.9937 | nan | 0.2623 | 0.9624 |
| 0.5766 | 110.0 | 330 | 0.3965 | 0.6192 | 0.6594 | 0.9621 | nan | 0.3272 | 0.9916 | nan | 0.2768 | 0.9616 |
| 0.3513 | 113.33 | 340 | 0.3927 | 0.6161 | 0.6523 | 0.9627 | nan | 0.3118 | 0.9928 | nan | 0.2701 | 0.9622 |
| 0.3731 | 116.67 | 350 | 0.3975 | 0.6200 | 0.6613 | 0.9620 | nan | 0.3315 | 0.9912 | nan | 0.2785 | 0.9614 |
| 0.3489 | 120.0 | 360 | 0.3958 | 0.6134 | 0.6480 | 0.9627 | nan | 0.3026 | 0.9933 | nan | 0.2645 | 0.9622 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|