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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGBD-b5_1
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-b5_1
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3428
- Mean Iou: 0.4792
- Mean Accuracy: 0.5000
- Overall Accuracy: 0.9583
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.0001
- Accuracy Undropoff: 0.9999
- Iou Unlabeled: nan
- Iou Dropoff: 0.0001
- Iou Undropoff: 0.9583
## 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: 3e-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.8047 | 5.0 | 10 | 0.9867 | 0.2744 | 0.6315 | 0.7475 | nan | 0.5049 | 0.7581 | 0.0 | 0.0812 | 0.7422 |
| 0.7528 | 10.0 | 20 | 0.8526 | 0.3461 | 0.5957 | 0.9213 | nan | 0.2406 | 0.9508 | 0.0 | 0.1178 | 0.9205 |
| 0.7087 | 15.0 | 30 | 0.7023 | 0.3450 | 0.5533 | 0.9467 | nan | 0.1243 | 0.9824 | 0.0 | 0.0887 | 0.9464 |
| 0.6601 | 20.0 | 40 | 0.6251 | 0.3381 | 0.5390 | 0.9462 | nan | 0.0948 | 0.9832 | 0.0 | 0.0684 | 0.9460 |
| 0.6274 | 25.0 | 50 | 0.5828 | 0.3286 | 0.5178 | 0.9486 | nan | 0.0479 | 0.9876 | 0.0 | 0.0374 | 0.9485 |
| 0.5929 | 30.0 | 60 | 0.5478 | 0.3257 | 0.5122 | 0.9488 | nan | 0.0359 | 0.9884 | 0.0 | 0.0284 | 0.9487 |
| 0.5672 | 35.0 | 70 | 0.5237 | 0.3240 | 0.5088 | 0.9494 | nan | 0.0283 | 0.9893 | 0.0 | 0.0227 | 0.9493 |
| 0.5454 | 40.0 | 80 | 0.4966 | 0.4856 | 0.5072 | 0.9529 | nan | 0.0212 | 0.9933 | nan | 0.0183 | 0.9528 |
| 0.5261 | 45.0 | 90 | 0.4700 | 0.3234 | 0.5062 | 0.9553 | nan | 0.0163 | 0.9960 | 0.0 | 0.0149 | 0.9552 |
| 0.5012 | 50.0 | 100 | 0.4576 | 0.4832 | 0.5041 | 0.9563 | nan | 0.0107 | 0.9974 | nan | 0.0101 | 0.9563 |
| 0.4875 | 55.0 | 110 | 0.4430 | 0.4811 | 0.5018 | 0.9566 | nan | 0.0058 | 0.9978 | nan | 0.0056 | 0.9565 |
| 0.4622 | 60.0 | 120 | 0.4328 | 0.4800 | 0.5007 | 0.9570 | nan | 0.0031 | 0.9983 | nan | 0.0030 | 0.9570 |
| 0.4394 | 65.0 | 130 | 0.4179 | 0.4796 | 0.5004 | 0.9572 | nan | 0.0021 | 0.9986 | nan | 0.0021 | 0.9572 |
| 0.4352 | 70.0 | 140 | 0.4048 | 0.4795 | 0.5002 | 0.9573 | nan | 0.0016 | 0.9988 | nan | 0.0016 | 0.9573 |
| 0.426 | 75.0 | 150 | 0.3881 | 0.4796 | 0.5003 | 0.9577 | nan | 0.0015 | 0.9992 | nan | 0.0014 | 0.9577 |
| 0.4175 | 80.0 | 160 | 0.3794 | 0.4797 | 0.5004 | 0.9579 | nan | 0.0014 | 0.9994 | nan | 0.0014 | 0.9579 |
| 0.4087 | 85.0 | 170 | 0.3742 | 0.3196 | 0.5002 | 0.9577 | nan | 0.0012 | 0.9992 | 0.0 | 0.0012 | 0.9577 |
| 0.3887 | 90.0 | 180 | 0.3645 | 0.4792 | 0.4999 | 0.9581 | nan | 0.0003 | 0.9996 | nan | 0.0003 | 0.9581 |
| 0.3799 | 95.0 | 190 | 0.3540 | 0.4791 | 0.4999 | 0.9581 | nan | 0.0001 | 0.9997 | nan | 0.0001 | 0.9581 |
| 0.376 | 100.0 | 200 | 0.3511 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 |
| 0.3677 | 105.0 | 210 | 0.3452 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 |
| 0.358 | 110.0 | 220 | 0.3437 | 0.4792 | 0.4999 | 0.9582 | nan | 0.0001 | 0.9998 | nan | 0.0001 | 0.9582 |
| 0.3997 | 115.0 | 230 | 0.3434 | 0.4792 | 0.5000 | 0.9583 | nan | 0.0001 | 0.9999 | nan | 0.0001 | 0.9583 |
| 0.3769 | 120.0 | 240 | 0.3428 | 0.4792 | 0.5000 | 0.9583 | nan | 0.0001 | 0.9999 | nan | 0.0001 | 0.9583 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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