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_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-b5_1

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: 0.6279
- Mean Iou: 0.4054
- Mean Accuracy: 0.7471
- Overall Accuracy: 0.8860
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.5956
- Accuracy Undropoff: 0.8986
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.3318
- Iou Undropoff: 0.8843

## 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.0071        | 5.0   | 10   | 1.0206          | 0.1745   | 0.2748        | 0.5034           | nan                | 0.0255           | 0.5241             | 0.0           | 0.0147      | 0.5087        |
| 0.9688        | 10.0  | 20   | 0.9873          | 0.2140   | 0.3486        | 0.5771           | nan                | 0.0992           | 0.5979             | 0.0           | 0.0582      | 0.5838        |
| 0.9406        | 15.0  | 30   | 0.9313          | 0.2613   | 0.4446        | 0.6655           | nan                | 0.2038           | 0.6855             | 0.0           | 0.1135      | 0.6705        |
| 0.9278        | 20.0  | 40   | 0.8851          | 0.2930   | 0.5149        | 0.7111           | nan                | 0.3009           | 0.7289             | 0.0           | 0.1648      | 0.7142        |
| 0.8956        | 25.0  | 50   | 0.8563          | 0.3118   | 0.5642        | 0.7358           | nan                | 0.3770           | 0.7514             | 0.0           | 0.1985      | 0.7370        |
| 0.8674        | 30.0  | 60   | 0.8260          | 0.3303   | 0.6086        | 0.7664           | nan                | 0.4366           | 0.7807             | 0.0           | 0.2246      | 0.7664        |
| 0.8438        | 35.0  | 70   | 0.8149          | 0.3347   | 0.6355        | 0.7671           | nan                | 0.4921           | 0.7790             | 0.0           | 0.2381      | 0.7660        |
| 0.8309        | 40.0  | 80   | 0.7881          | 0.3459   | 0.6472        | 0.7847           | nan                | 0.4972           | 0.7972             | 0.0           | 0.2539      | 0.7839        |
| 0.8069        | 45.0  | 90   | 0.7640          | 0.3567   | 0.6617        | 0.8041           | nan                | 0.5063           | 0.8170             | 0.0           | 0.2668      | 0.8033        |
| 0.7779        | 50.0  | 100  | 0.7486          | 0.3637   | 0.6792        | 0.8145           | nan                | 0.5316           | 0.8268             | 0.0           | 0.2778      | 0.8132        |
| 0.7695        | 55.0  | 110  | 0.7354          | 0.3684   | 0.6936        | 0.8214           | nan                | 0.5542           | 0.8329             | 0.0           | 0.2858      | 0.8195        |
| 0.7568        | 60.0  | 120  | 0.7164          | 0.3757   | 0.7032        | 0.8365           | nan                | 0.5577           | 0.8486             | 0.0           | 0.2924      | 0.8347        |
| 0.7285        | 65.0  | 130  | 0.6976          | 0.3836   | 0.7119        | 0.8484           | nan                | 0.5630           | 0.8608             | 0.0           | 0.3042      | 0.8467        |
| 0.7217        | 70.0  | 140  | 0.6922          | 0.3857   | 0.7217        | 0.8499           | nan                | 0.5817           | 0.8616             | 0.0           | 0.3091      | 0.8480        |
| 0.7095        | 75.0  | 150  | 0.6708          | 0.3926   | 0.7287        | 0.8624           | nan                | 0.5828           | 0.8745             | 0.0           | 0.3172      | 0.8605        |
| 0.6944        | 80.0  | 160  | 0.6637          | 0.3951   | 0.7320        | 0.8660           | nan                | 0.5858           | 0.8781             | 0.0           | 0.3212      | 0.8641        |
| 0.6878        | 85.0  | 170  | 0.6632          | 0.3942   | 0.7397        | 0.8673           | nan                | 0.6005           | 0.8788             | 0.0           | 0.3175      | 0.8652        |
| 0.6868        | 90.0  | 180  | 0.6468          | 0.3998   | 0.7391        | 0.8756           | nan                | 0.5902           | 0.8880             | 0.0           | 0.3257      | 0.8739        |
| 0.6581        | 95.0  | 190  | 0.6444          | 0.4003   | 0.7421        | 0.8776           | nan                | 0.5942           | 0.8899             | 0.0           | 0.3249      | 0.8759        |
| 0.6587        | 100.0 | 200  | 0.6383          | 0.4026   | 0.7427        | 0.8814           | nan                | 0.5914           | 0.8940             | 0.0           | 0.3281      | 0.8797        |
| 0.6525        | 105.0 | 210  | 0.6334          | 0.4032   | 0.7434        | 0.8825           | nan                | 0.5918           | 0.8951             | 0.0           | 0.3289      | 0.8808        |
| 0.658         | 110.0 | 220  | 0.6345          | 0.4026   | 0.7451        | 0.8811           | nan                | 0.5968           | 0.8934             | 0.0           | 0.3285      | 0.8793        |
| 0.6575        | 115.0 | 230  | 0.6300          | 0.4050   | 0.7463        | 0.8851           | nan                | 0.5948           | 0.8977             | 0.0           | 0.3314      | 0.8835        |
| 0.6625        | 120.0 | 240  | 0.6279          | 0.4054   | 0.7471        | 0.8860           | nan                | 0.5956           | 0.8986             | 0.0           | 0.3318      | 0.8843        |


### Framework versions

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