File size: 6,683 Bytes
31fcf73 |
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 |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGBD-b0_6
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-RGBD-b0_6
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2353
- Mean Iou: 0.6539
- Mean Accuracy: 0.7065
- Overall Accuracy: 0.9662
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.4233
- Accuracy Undropoff: 0.9897
- Iou Unlabeled: nan
- Iou Dropoff: 0.3423
- Iou Undropoff: 0.9656
## 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: 7e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.9975 | 5.0 | 10 | 1.0470 | 0.2819 | 0.6747 | 0.7186 | nan | 0.6267 | 0.7226 | 0.0 | 0.1290 | 0.7167 |
| 0.8329 | 10.0 | 20 | 0.8435 | 0.3211 | 0.5026 | 0.9526 | nan | 0.0117 | 0.9934 | 0.0 | 0.0106 | 0.9526 |
| 0.6857 | 15.0 | 30 | 0.6184 | 0.3191 | 0.4994 | 0.9567 | nan | 0.0006 | 0.9981 | 0.0 | 0.0006 | 0.9567 |
| 0.5913 | 20.0 | 40 | 0.4793 | 0.3193 | 0.4997 | 0.9573 | nan | 0.0005 | 0.9988 | 0.0 | 0.0005 | 0.9573 |
| 0.5299 | 25.0 | 50 | 0.4529 | 0.3488 | 0.5442 | 0.9596 | nan | 0.0911 | 0.9973 | 0.0 | 0.0869 | 0.9595 |
| 0.4922 | 30.0 | 60 | 0.4037 | 0.4352 | 0.6983 | 0.9671 | nan | 0.4051 | 0.9915 | 0.0 | 0.3390 | 0.9666 |
| 0.4769 | 35.0 | 70 | 0.4161 | 0.4090 | 0.7560 | 0.9426 | nan | 0.5524 | 0.9595 | 0.0 | 0.2858 | 0.9412 |
| 0.3916 | 40.0 | 80 | 0.3343 | 0.6320 | 0.6946 | 0.9614 | nan | 0.4036 | 0.9856 | nan | 0.3033 | 0.9608 |
| 0.3567 | 45.0 | 90 | 0.3372 | 0.6374 | 0.7140 | 0.9598 | nan | 0.4458 | 0.9821 | nan | 0.3157 | 0.9591 |
| 0.3234 | 50.0 | 100 | 0.3074 | 0.6402 | 0.6883 | 0.9652 | nan | 0.3863 | 0.9903 | nan | 0.3157 | 0.9646 |
| 0.3181 | 55.0 | 110 | 0.3043 | 0.6396 | 0.7138 | 0.9606 | nan | 0.4446 | 0.9830 | nan | 0.3194 | 0.9599 |
| 0.2584 | 60.0 | 120 | 0.3069 | 0.6450 | 0.7204 | 0.9613 | nan | 0.4576 | 0.9831 | nan | 0.3294 | 0.9605 |
| 0.2566 | 65.0 | 130 | 0.2824 | 0.6431 | 0.7063 | 0.9630 | nan | 0.4263 | 0.9863 | nan | 0.3239 | 0.9623 |
| 0.2353 | 70.0 | 140 | 0.2763 | 0.6470 | 0.7046 | 0.9645 | nan | 0.4212 | 0.9880 | nan | 0.3301 | 0.9638 |
| 0.2368 | 75.0 | 150 | 0.2644 | 0.6474 | 0.6973 | 0.9658 | nan | 0.4044 | 0.9902 | nan | 0.3296 | 0.9652 |
| 0.2225 | 80.0 | 160 | 0.2673 | 0.6462 | 0.7089 | 0.9635 | nan | 0.4313 | 0.9866 | nan | 0.3296 | 0.9629 |
| 0.1976 | 85.0 | 170 | 0.2568 | 0.6449 | 0.7057 | 0.9637 | nan | 0.4244 | 0.9870 | nan | 0.3268 | 0.9630 |
| 0.1981 | 90.0 | 180 | 0.2572 | 0.6444 | 0.7110 | 0.9626 | nan | 0.4365 | 0.9855 | nan | 0.3269 | 0.9619 |
| 0.1857 | 95.0 | 190 | 0.2503 | 0.6504 | 0.7027 | 0.9658 | nan | 0.4157 | 0.9897 | nan | 0.3356 | 0.9652 |
| 0.1826 | 100.0 | 200 | 0.2345 | 0.6509 | 0.6984 | 0.9666 | nan | 0.4059 | 0.9909 | nan | 0.3357 | 0.9660 |
| 0.1818 | 105.0 | 210 | 0.2484 | 0.6506 | 0.7160 | 0.9637 | nan | 0.4458 | 0.9862 | nan | 0.3381 | 0.9630 |
| 0.1919 | 110.0 | 220 | 0.2343 | 0.6526 | 0.6996 | 0.9669 | nan | 0.4080 | 0.9912 | nan | 0.3389 | 0.9663 |
| 0.17 | 115.0 | 230 | 0.2377 | 0.6535 | 0.7065 | 0.9661 | nan | 0.4235 | 0.9896 | nan | 0.3416 | 0.9655 |
| 0.1739 | 120.0 | 240 | 0.2353 | 0.6539 | 0.7065 | 0.9662 | nan | 0.4233 | 0.9897 | nan | 0.3423 | 0.9656 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|