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-b5_4
  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-b5_4

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: 0.2242
- Mean Iou: 0.4568
- Mean Accuracy: 0.7402
- Overall Accuracy: 0.9696
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.4899
- Accuracy Undropoff: 0.9904
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.4016
- Iou Undropoff: 0.9690

## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.9465        | 5.0   | 10   | 0.9974          | 0.2695   | 0.5001        | 0.6771           | nan                | 0.3071           | 0.6931             | 0.0           | 0.1261      | 0.6824        |
| 0.8558        | 10.0  | 20   | 0.8237          | 0.3822   | 0.7119        | 0.8664           | nan                | 0.5434           | 0.8804             | 0.0           | 0.2787      | 0.8678        |
| 0.7585        | 15.0  | 30   | 0.6801          | 0.4232   | 0.7487        | 0.9194           | nan                | 0.5625           | 0.9349             | 0.0           | 0.3494      | 0.9202        |
| 0.715         | 20.0  | 40   | 0.6076          | 0.4298   | 0.7663        | 0.9232           | nan                | 0.5952           | 0.9375             | 0.0           | 0.3661      | 0.9233        |
| 0.6145        | 25.0  | 50   | 0.5298          | 0.4398   | 0.7760        | 0.9380           | nan                | 0.5994           | 0.9527             | 0.0           | 0.3819      | 0.9375        |
| 0.5355        | 30.0  | 60   | 0.4821          | 0.4426   | 0.7749        | 0.9428           | nan                | 0.5918           | 0.9581             | 0.0           | 0.3857      | 0.9422        |
| 0.4619        | 35.0  | 70   | 0.4266          | 0.4493   | 0.7716        | 0.9524           | nan                | 0.5743           | 0.9688             | 0.0           | 0.3962      | 0.9517        |
| 0.4367        | 40.0  | 80   | 0.3941          | 0.4519   | 0.7738        | 0.9568           | nan                | 0.5742           | 0.9734             | 0.0           | 0.3997      | 0.9559        |
| 0.3839        | 45.0  | 90   | 0.3801          | 0.4528   | 0.7796        | 0.9577           | nan                | 0.5853           | 0.9738             | 0.0           | 0.4017      | 0.9567        |
| 0.3164        | 50.0  | 100  | 0.3549          | 0.4543   | 0.7785        | 0.9608           | nan                | 0.5797           | 0.9773             | 0.0           | 0.4030      | 0.9599        |
| 0.3018        | 55.0  | 110  | 0.3327          | 0.4573   | 0.7731        | 0.9639           | nan                | 0.5650           | 0.9812             | 0.0           | 0.4087      | 0.9631        |
| 0.2646        | 60.0  | 120  | 0.3127          | 0.4590   | 0.7703        | 0.9658           | nan                | 0.5571           | 0.9835             | 0.0           | 0.4121      | 0.9650        |
| 0.2378        | 65.0  | 130  | 0.2958          | 0.4628   | 0.7728        | 0.9673           | nan                | 0.5607           | 0.9850             | 0.0           | 0.4217      | 0.9666        |
| 0.2076        | 70.0  | 140  | 0.2778          | 0.4675   | 0.7729        | 0.9693           | nan                | 0.5586           | 0.9871             | 0.0           | 0.4340      | 0.9686        |
| 0.1951        | 75.0  | 150  | 0.2648          | 0.4666   | 0.7719        | 0.9692           | nan                | 0.5567           | 0.9871             | 0.0           | 0.4314      | 0.9685        |
| 0.1734        | 80.0  | 160  | 0.2522          | 0.4673   | 0.7643        | 0.9703           | nan                | 0.5397           | 0.9890             | 0.0           | 0.4322      | 0.9696        |
| 0.1569        | 85.0  | 170  | 0.2436          | 0.4660   | 0.7603        | 0.9703           | nan                | 0.5312           | 0.9894             | 0.0           | 0.4282      | 0.9697        |
| 0.1691        | 90.0  | 180  | 0.2411          | 0.4647   | 0.7624        | 0.9697           | nan                | 0.5363           | 0.9885             | 0.0           | 0.4250      | 0.9690        |
| 0.1498        | 95.0  | 190  | 0.2335          | 0.4623   | 0.7537        | 0.9699           | nan                | 0.5179           | 0.9895             | 0.0           | 0.4176      | 0.9692        |
| 0.1478        | 100.0 | 200  | 0.2281          | 0.4585   | 0.7420        | 0.9700           | nan                | 0.4934           | 0.9906             | 0.0           | 0.4062      | 0.9693        |
| 0.1407        | 105.0 | 210  | 0.2278          | 0.4615   | 0.7501        | 0.9701           | nan                | 0.5102           | 0.9900             | 0.0           | 0.4151      | 0.9694        |
| 0.1397        | 110.0 | 220  | 0.2305          | 0.4610   | 0.7512        | 0.9698           | nan                | 0.5129           | 0.9896             | 0.0           | 0.4140      | 0.9691        |
| 0.1317        | 115.0 | 230  | 0.2265          | 0.4576   | 0.7430        | 0.9695           | nan                | 0.4959           | 0.9901             | 0.0           | 0.4038      | 0.9689        |
| 0.1548        | 120.0 | 240  | 0.2242          | 0.4568   | 0.7402        | 0.9696           | nan                | 0.4899           | 0.9904             | 0.0           | 0.4016      | 0.9690        |


### Framework versions

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