transformers pytorch tensorboard segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGB-b0_7
  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_7

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: 0.1457
- Mean Iou: 0.6795
- Mean Accuracy: 0.7207
- Overall Accuracy: 0.9691
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.4481
- Accuracy Undropoff: 0.9932
- Iou Unlabeled: nan
- Iou Dropoff: 0.3907
- Iou Undropoff: 0.9684

## 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-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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.1505        | 3.33   | 10   | 1.1103          | 0.1106   | 0.6036        | 0.2919           | nan                | 0.9456           | 0.2616             | 0.0           | 0.0703      | 0.2616        |
| 0.9635        | 6.67   | 20   | 1.0114          | 0.3710   | 0.8470        | 0.8737           | nan                | 0.8177           | 0.8763             | 0.0           | 0.2435      | 0.8694        |
| 0.9358        | 10.0   | 30   | 0.8242          | 0.4206   | 0.7727        | 0.9440           | nan                | 0.5848           | 0.9606             | 0.0           | 0.3194      | 0.9425        |
| 0.579         | 13.33  | 40   | 0.5703          | 0.4525   | 0.7615        | 0.9633           | nan                | 0.5402           | 0.9829             | 0.0           | 0.3951      | 0.9624        |
| 0.4411        | 16.67  | 50   | 0.4166          | 0.4529   | 0.7380        | 0.9667           | nan                | 0.4872           | 0.9889             | 0.0           | 0.3928      | 0.9659        |
| 0.4311        | 20.0   | 60   | 0.3843          | 0.6678   | 0.7156        | 0.9667           | nan                | 0.4400           | 0.9911             | nan           | 0.3695      | 0.9661        |
| 0.3437        | 23.33  | 70   | 0.3590          | 0.4347   | 0.6956        | 0.9655           | nan                | 0.3995           | 0.9918             | 0.0           | 0.3392      | 0.9649        |
| 0.3136        | 26.67  | 80   | 0.3198          | 0.6259   | 0.6622        | 0.9638           | nan                | 0.3312           | 0.9931             | nan           | 0.2885      | 0.9633        |
| 0.2682        | 30.0   | 90   | 0.2919          | 0.6187   | 0.6470        | 0.9648           | nan                | 0.2984           | 0.9957             | nan           | 0.2730      | 0.9643        |
| 0.2521        | 33.33  | 100  | 0.2957          | 0.6448   | 0.6845        | 0.9653           | nan                | 0.3764           | 0.9926             | nan           | 0.3248      | 0.9648        |
| 0.2287        | 36.67  | 110  | 0.2747          | 0.6800   | 0.7256        | 0.9685           | nan                | 0.4591           | 0.9921             | nan           | 0.3922      | 0.9678        |
| 0.2203        | 40.0   | 120  | 0.2537          | 0.7108   | 0.7687        | 0.9706           | nan                | 0.5472           | 0.9902             | nan           | 0.4517      | 0.9699        |
| 0.1964        | 43.33  | 130  | 0.2356          | 0.6689   | 0.7054        | 0.9686           | nan                | 0.4167           | 0.9941             | nan           | 0.3699      | 0.9680        |
| 0.1776        | 46.67  | 140  | 0.2205          | 0.6729   | 0.7137        | 0.9684           | nan                | 0.4343           | 0.9931             | nan           | 0.3780      | 0.9677        |
| 0.1675        | 50.0   | 150  | 0.2061          | 0.6809   | 0.7244        | 0.9689           | nan                | 0.4562           | 0.9926             | nan           | 0.3936      | 0.9682        |
| 0.148         | 53.33  | 160  | 0.1954          | 0.6924   | 0.7418        | 0.9694           | nan                | 0.4920           | 0.9915             | nan           | 0.4160      | 0.9687        |
| 0.1364        | 56.67  | 170  | 0.1915          | 0.6869   | 0.7415        | 0.9681           | nan                | 0.4928           | 0.9902             | nan           | 0.4064      | 0.9674        |
| 0.1171        | 60.0   | 180  | 0.1776          | 0.7206   | 0.7816        | 0.9714           | nan                | 0.5734           | 0.9899             | nan           | 0.4706      | 0.9707        |
| 0.1169        | 63.33  | 190  | 0.1754          | 0.6580   | 0.6853        | 0.9689           | nan                | 0.3741           | 0.9965             | nan           | 0.3476      | 0.9684        |
| 0.1178        | 66.67  | 200  | 0.1676          | 0.6783   | 0.7233        | 0.9684           | nan                | 0.4545           | 0.9922             | nan           | 0.3888      | 0.9677        |
| 0.1016        | 70.0   | 210  | 0.1670          | 0.6633   | 0.6985        | 0.9682           | nan                | 0.4025           | 0.9944             | nan           | 0.3590      | 0.9676        |
| 0.1025        | 73.33  | 220  | 0.1648          | 0.6789   | 0.7154        | 0.9696           | nan                | 0.4366           | 0.9943             | nan           | 0.3888      | 0.9690        |
| 0.0956        | 76.67  | 230  | 0.1607          | 0.6684   | 0.7103        | 0.9677           | nan                | 0.4279           | 0.9927             | nan           | 0.3697      | 0.9671        |
| 0.1443        | 80.0   | 240  | 0.1611          | 0.6747   | 0.7134        | 0.9688           | nan                | 0.4332           | 0.9937             | nan           | 0.3811      | 0.9682        |
| 0.0902        | 83.33  | 250  | 0.1600          | 0.6713   | 0.7060        | 0.9691           | nan                | 0.4174           | 0.9946             | nan           | 0.3740      | 0.9685        |
| 0.0846        | 86.67  | 260  | 0.1559          | 0.6772   | 0.7263        | 0.9677           | nan                | 0.4613           | 0.9912             | nan           | 0.3874      | 0.9670        |
| 0.1166        | 90.0   | 270  | 0.1587          | 0.6615   | 0.6984        | 0.9677           | nan                | 0.4030           | 0.9939             | nan           | 0.3559      | 0.9671        |
| 0.0825        | 93.33  | 280  | 0.1538          | 0.6684   | 0.7068        | 0.9682           | nan                | 0.4199           | 0.9936             | nan           | 0.3692      | 0.9676        |
| 0.0769        | 96.67  | 290  | 0.1527          | 0.6649   | 0.7033        | 0.9679           | nan                | 0.4130           | 0.9936             | nan           | 0.3626      | 0.9673        |
| 0.0722        | 100.0  | 300  | 0.1473          | 0.6832   | 0.7247        | 0.9694           | nan                | 0.4563           | 0.9932             | nan           | 0.3976      | 0.9688        |
| 0.0779        | 103.33 | 310  | 0.1465          | 0.6809   | 0.7200        | 0.9695           | nan                | 0.4462           | 0.9937             | nan           | 0.3930      | 0.9689        |
| 0.0771        | 106.67 | 320  | 0.1494          | 0.6673   | 0.7052        | 0.9682           | nan                | 0.4167           | 0.9937             | nan           | 0.3670      | 0.9676        |
| 0.1082        | 110.0  | 330  | 0.1479          | 0.6753   | 0.7182        | 0.9683           | nan                | 0.4438           | 0.9926             | nan           | 0.3830      | 0.9677        |
| 0.0726        | 113.33 | 340  | 0.1451          | 0.6765   | 0.7159        | 0.9689           | nan                | 0.4384           | 0.9935             | nan           | 0.3846      | 0.9683        |
| 0.0743        | 116.67 | 350  | 0.1469          | 0.6814   | 0.7249        | 0.9689           | nan                | 0.4571           | 0.9927             | nan           | 0.3946      | 0.9683        |
| 0.0703        | 120.0  | 360  | 0.1457          | 0.6795   | 0.7207        | 0.9691           | nan                | 0.4481           | 0.9932             | nan           | 0.3907      | 0.9684        |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
