|
--- |
|
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 |
|
|
