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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGB-b0_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-RGB-b0_1
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: 0.5626
- Mean Iou: 0.4261
- Mean Accuracy: 0.7046
- Overall Accuracy: 0.9598
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.4247
- Accuracy Undropoff: 0.9846
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.3192
- Iou Undropoff: 0.9590
## 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: 9e-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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.1029 | 3.33 | 10 | 1.0852 | 0.1637 | 0.3955 | 0.4522 | nan | 0.3333 | 0.4577 | 0.0 | 0.0410 | 0.4501 |
| 1.0856 | 6.67 | 20 | 1.0764 | 0.1911 | 0.5086 | 0.5025 | nan | 0.5153 | 0.5019 | 0.0 | 0.0761 | 0.4972 |
| 1.0755 | 10.0 | 30 | 1.0611 | 0.2252 | 0.6367 | 0.5749 | nan | 0.7045 | 0.5688 | 0.0 | 0.1104 | 0.5652 |
| 1.0285 | 13.33 | 40 | 1.0382 | 0.2622 | 0.7487 | 0.6568 | nan | 0.8494 | 0.6479 | 0.0 | 0.1420 | 0.6445 |
| 0.9935 | 16.67 | 50 | 1.0151 | 0.2893 | 0.7814 | 0.7201 | nan | 0.8486 | 0.7141 | 0.0 | 0.1580 | 0.7099 |
| 0.9927 | 20.0 | 60 | 0.9834 | 0.3160 | 0.7963 | 0.7816 | nan | 0.8124 | 0.7801 | 0.0 | 0.1735 | 0.7744 |
| 0.938 | 23.33 | 70 | 0.9585 | 0.3308 | 0.8084 | 0.8127 | nan | 0.8036 | 0.8131 | 0.0 | 0.1860 | 0.8065 |
| 0.9169 | 26.67 | 80 | 0.9376 | 0.3457 | 0.8169 | 0.8376 | nan | 0.7943 | 0.8396 | 0.0 | 0.2048 | 0.8324 |
| 0.8814 | 30.0 | 90 | 0.9003 | 0.3624 | 0.8086 | 0.8691 | nan | 0.7421 | 0.8750 | 0.0 | 0.2220 | 0.8651 |
| 0.8618 | 33.33 | 100 | 0.8894 | 0.3669 | 0.8184 | 0.8761 | nan | 0.7550 | 0.8817 | 0.0 | 0.2287 | 0.8720 |
| 0.8388 | 36.67 | 110 | 0.8618 | 0.3774 | 0.8096 | 0.8926 | nan | 0.7187 | 0.9006 | 0.0 | 0.2431 | 0.8892 |
| 0.8878 | 40.0 | 120 | 0.8269 | 0.3929 | 0.7937 | 0.9140 | nan | 0.6618 | 0.9257 | 0.0 | 0.2671 | 0.9116 |
| 0.8066 | 43.33 | 130 | 0.8074 | 0.4014 | 0.7955 | 0.9225 | nan | 0.6562 | 0.9348 | 0.0 | 0.2839 | 0.9202 |
| 0.8084 | 46.67 | 140 | 0.7919 | 0.4023 | 0.7932 | 0.9248 | nan | 0.6487 | 0.9376 | 0.0 | 0.2844 | 0.9226 |
| 0.7415 | 50.0 | 150 | 0.7707 | 0.4068 | 0.7850 | 0.9309 | nan | 0.6249 | 0.9451 | 0.0 | 0.2913 | 0.9290 |
| 0.7508 | 53.33 | 160 | 0.7326 | 0.4154 | 0.7660 | 0.9415 | nan | 0.5735 | 0.9585 | 0.0 | 0.3063 | 0.9400 |
| 0.7312 | 56.67 | 170 | 0.7126 | 0.4196 | 0.7636 | 0.9449 | nan | 0.5646 | 0.9625 | 0.0 | 0.3155 | 0.9435 |
| 0.6442 | 60.0 | 180 | 0.6869 | 0.4255 | 0.7500 | 0.9509 | nan | 0.5296 | 0.9704 | 0.0 | 0.3268 | 0.9497 |
| 0.6633 | 63.33 | 190 | 0.6765 | 0.4286 | 0.7524 | 0.9525 | nan | 0.5328 | 0.9719 | 0.0 | 0.3343 | 0.9513 |
| 0.7247 | 66.67 | 200 | 0.6557 | 0.4307 | 0.7335 | 0.9568 | nan | 0.4886 | 0.9785 | 0.0 | 0.3364 | 0.9558 |
| 0.6133 | 70.0 | 210 | 0.6369 | 0.4298 | 0.7279 | 0.9573 | nan | 0.4761 | 0.9796 | 0.0 | 0.3330 | 0.9564 |
| 0.6309 | 73.33 | 220 | 0.6309 | 0.4298 | 0.7437 | 0.9547 | nan | 0.5123 | 0.9752 | 0.0 | 0.3356 | 0.9536 |
| 0.6373 | 76.67 | 230 | 0.6094 | 0.4276 | 0.7197 | 0.9577 | nan | 0.4585 | 0.9808 | 0.0 | 0.3262 | 0.9568 |
| 0.8436 | 80.0 | 240 | 0.6195 | 0.4341 | 0.7438 | 0.9569 | nan | 0.5101 | 0.9776 | 0.0 | 0.3463 | 0.9559 |
| 0.6172 | 83.33 | 250 | 0.6207 | 0.4323 | 0.7384 | 0.9570 | nan | 0.4987 | 0.9782 | 0.0 | 0.3409 | 0.9560 |
| 0.6048 | 86.67 | 260 | 0.5949 | 0.4272 | 0.7136 | 0.9586 | nan | 0.4449 | 0.9824 | 0.0 | 0.3237 | 0.9578 |
| 0.7887 | 90.0 | 270 | 0.6007 | 0.4308 | 0.7282 | 0.9580 | nan | 0.4760 | 0.9803 | 0.0 | 0.3353 | 0.9571 |
| 0.605 | 93.33 | 280 | 0.5883 | 0.4284 | 0.7157 | 0.9589 | nan | 0.4489 | 0.9825 | 0.0 | 0.3271 | 0.9581 |
| 0.5964 | 96.67 | 290 | 0.5872 | 0.4277 | 0.7134 | 0.9590 | nan | 0.4439 | 0.9828 | 0.0 | 0.3251 | 0.9581 |
| 0.6097 | 100.0 | 300 | 0.5903 | 0.4300 | 0.7240 | 0.9582 | nan | 0.4669 | 0.9810 | 0.0 | 0.3325 | 0.9573 |
| 0.5886 | 103.33 | 310 | 0.5710 | 0.4250 | 0.7035 | 0.9594 | nan | 0.4227 | 0.9843 | 0.0 | 0.3162 | 0.9586 |
| 0.6079 | 106.67 | 320 | 0.5695 | 0.4277 | 0.7112 | 0.9594 | nan | 0.4390 | 0.9835 | 0.0 | 0.3245 | 0.9586 |
| 0.8054 | 110.0 | 330 | 0.5746 | 0.4308 | 0.7237 | 0.9588 | nan | 0.4657 | 0.9816 | 0.0 | 0.3344 | 0.9579 |
| 0.5496 | 113.33 | 340 | 0.5631 | 0.4285 | 0.7129 | 0.9595 | nan | 0.4424 | 0.9835 | 0.0 | 0.3269 | 0.9587 |
| 0.6271 | 116.67 | 350 | 0.5761 | 0.4302 | 0.7214 | 0.9589 | nan | 0.4608 | 0.9819 | 0.0 | 0.3326 | 0.9580 |
| 0.5511 | 120.0 | 360 | 0.5626 | 0.4261 | 0.7046 | 0.9598 | nan | 0.4247 | 0.9846 | 0.0 | 0.3192 | 0.9590 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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