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_3
  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_3

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: 0.3770
- Mean Iou: 0.4572
- Mean Accuracy: 0.7822
- Overall Accuracy: 0.9640
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.5839
- Accuracy Undropoff: 0.9805
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.4086
- Iou Undropoff: 0.9631

## 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: 5e-06
- train_batch_size: 15
- eval_batch_size: 15
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.3135        | 5.0   | 10   | 1.2008          | 0.0546   | 0.2586        | 0.1227           | nan                | 0.4069           | 0.1103             | 0.0           | 0.0535      | 0.1102        |
| 1.2309        | 10.0  | 20   | 1.1294          | 0.1176   | 0.3397        | 0.2490           | nan                | 0.4388           | 0.2407             | 0.0           | 0.1129      | 0.2400        |
| 1.1346        | 15.0  | 30   | 1.0395          | 0.2171   | 0.4865        | 0.5022           | nan                | 0.4694           | 0.5036             | 0.0           | 0.1524      | 0.4989        |
| 1.1088        | 20.0  | 40   | 0.9755          | 0.2608   | 0.5521        | 0.6176           | nan                | 0.4808           | 0.6235             | 0.0           | 0.1661      | 0.6163        |
| 1.007         | 25.0  | 50   | 0.9197          | 0.2895   | 0.5959        | 0.6775           | nan                | 0.5068           | 0.6849             | 0.0           | 0.1923      | 0.6763        |
| 0.9145        | 30.0  | 60   | 0.8635          | 0.3162   | 0.6299        | 0.7335           | nan                | 0.5168           | 0.7429             | 0.0           | 0.2156      | 0.7329        |
| 0.8745        | 35.0  | 70   | 0.8070          | 0.3398   | 0.6784        | 0.7808           | nan                | 0.5667           | 0.7901             | 0.0           | 0.2404      | 0.7791        |
| 0.8088        | 40.0  | 80   | 0.7442          | 0.3667   | 0.7191        | 0.8290           | nan                | 0.5993           | 0.8389             | 0.0           | 0.2730      | 0.8272        |
| 0.7184        | 45.0  | 90   | 0.6956          | 0.3832   | 0.7513        | 0.8603           | nan                | 0.6323           | 0.8702             | 0.0           | 0.2915      | 0.8580        |
| 0.6908        | 50.0  | 100  | 0.6751          | 0.3931   | 0.7592        | 0.8748           | nan                | 0.6332           | 0.8853             | 0.0           | 0.3067      | 0.8728        |
| 0.643         | 55.0  | 110  | 0.6101          | 0.4134   | 0.7714        | 0.9108           | nan                | 0.6194           | 0.9234             | 0.0           | 0.3308      | 0.9094        |
| 0.6014        | 60.0  | 120  | 0.5971          | 0.4166   | 0.7826        | 0.9189           | nan                | 0.6339           | 0.9313             | 0.0           | 0.3324      | 0.9175        |
| 0.5685        | 65.0  | 130  | 0.5595          | 0.4304   | 0.7946        | 0.9328           | nan                | 0.6439           | 0.9453             | 0.0           | 0.3599      | 0.9314        |
| 0.5172        | 70.0  | 140  | 0.5344          | 0.4373   | 0.8010        | 0.9406           | nan                | 0.6488           | 0.9532             | 0.0           | 0.3727      | 0.9393        |
| 0.4757        | 75.0  | 150  | 0.4963          | 0.4434   | 0.7997        | 0.9490           | nan                | 0.6368           | 0.9626             | 0.0           | 0.3822      | 0.9479        |
| 0.4288        | 80.0  | 160  | 0.4599          | 0.4488   | 0.7936        | 0.9556           | nan                | 0.6169           | 0.9702             | 0.0           | 0.3918      | 0.9546        |
| 0.4124        | 85.0  | 170  | 0.4710          | 0.4469   | 0.7989        | 0.9540           | nan                | 0.6296           | 0.9681             | 0.0           | 0.3876      | 0.9529        |
| 0.4995        | 90.0  | 180  | 0.4209          | 0.4537   | 0.7883        | 0.9606           | nan                | 0.6004           | 0.9762             | 0.0           | 0.4015      | 0.9597        |
| 0.3815        | 95.0  | 190  | 0.4287          | 0.4524   | 0.7919        | 0.9595           | nan                | 0.6090           | 0.9748             | 0.0           | 0.3988      | 0.9586        |
| 0.3764        | 100.0 | 200  | 0.4245          | 0.4529   | 0.7913        | 0.9600           | nan                | 0.6073           | 0.9753             | 0.0           | 0.3998      | 0.9590        |
| 0.4074        | 105.0 | 210  | 0.4096          | 0.4542   | 0.7894        | 0.9613           | nan                | 0.6018           | 0.9769             | 0.0           | 0.4021      | 0.9603        |
| 0.3975        | 110.0 | 220  | 0.4107          | 0.4538   | 0.7905        | 0.9610           | nan                | 0.6045           | 0.9765             | 0.0           | 0.4013      | 0.9601        |
| 0.3598        | 115.0 | 230  | 0.3918          | 0.4558   | 0.7863        | 0.9627           | nan                | 0.5939           | 0.9787             | 0.0           | 0.4057      | 0.9618        |
| 0.3709        | 120.0 | 240  | 0.3770          | 0.4572   | 0.7822        | 0.9640           | nan                | 0.5839           | 0.9805             | 0.0           | 0.4086      | 0.9631        |


### Framework versions

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