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-RGBD-b5_5
  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-RGBD-b5_5

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: 0.2636
- Mean Iou: 0.4256
- Mean Accuracy: 0.6832
- Overall Accuracy: 0.9656
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.3752
- Accuracy Undropoff: 0.9912
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.3118
- Iou Undropoff: 0.9650

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.1487        | 5.0   | 10   | 1.0250          | 0.2562   | 0.6276        | 0.6778           | nan                | 0.5730           | 0.6823             | 0.0           | 0.0971      | 0.6714        |
| 1.0128        | 10.0  | 20   | 0.9030          | 0.3142   | 0.6730        | 0.8268           | nan                | 0.5053           | 0.8407             | 0.0           | 0.1195      | 0.8231        |
| 0.8561        | 15.0  | 30   | 0.7359          | 0.3520   | 0.6913        | 0.8949           | nan                | 0.4692           | 0.9133             | 0.0           | 0.1632      | 0.8928        |
| 0.7551        | 20.0  | 40   | 0.6534          | 0.3634   | 0.6999        | 0.9090           | nan                | 0.4719           | 0.9280             | 0.0           | 0.1829      | 0.9072        |
| 0.6236        | 25.0  | 50   | 0.5938          | 0.3710   | 0.7001        | 0.9189           | nan                | 0.4614           | 0.9388             | 0.0           | 0.1955      | 0.9173        |
| 0.4977        | 30.0  | 60   | 0.5293          | 0.3850   | 0.6987        | 0.9341           | nan                | 0.4420           | 0.9555             | 0.0           | 0.2222      | 0.9329        |
| 0.4188        | 35.0  | 70   | 0.4859          | 0.3935   | 0.6941        | 0.9425           | nan                | 0.4231           | 0.9650             | 0.0           | 0.2390      | 0.9415        |
| 0.3532        | 40.0  | 80   | 0.4278          | 0.4019   | 0.6823        | 0.9519           | nan                | 0.3881           | 0.9764             | 0.0           | 0.2547      | 0.9511        |
| 0.3187        | 45.0  | 90   | 0.3914          | 0.4098   | 0.6873        | 0.9560           | nan                | 0.3942           | 0.9804             | 0.0           | 0.2742      | 0.9553        |
| 0.2631        | 50.0  | 100  | 0.3647          | 0.4134   | 0.6918        | 0.9575           | nan                | 0.4020           | 0.9815             | 0.0           | 0.2835      | 0.9567        |
| 0.2565        | 55.0  | 110  | 0.3424          | 0.4141   | 0.6895        | 0.9585           | nan                | 0.3962           | 0.9829             | 0.0           | 0.2846      | 0.9578        |
| 0.2259        | 60.0  | 120  | 0.3127          | 0.4178   | 0.6853        | 0.9613           | nan                | 0.3843           | 0.9863             | 0.0           | 0.2926      | 0.9607        |
| 0.2263        | 65.0  | 130  | 0.2920          | 0.4202   | 0.6822        | 0.9632           | nan                | 0.3757           | 0.9886             | 0.0           | 0.2981      | 0.9626        |
| 0.1961        | 70.0  | 140  | 0.2755          | 0.4218   | 0.6769        | 0.9649           | nan                | 0.3627           | 0.9911             | 0.0           | 0.3009      | 0.9644        |
| 0.1897        | 75.0  | 150  | 0.2726          | 0.4232   | 0.6803        | 0.9650           | nan                | 0.3698           | 0.9908             | 0.0           | 0.3052      | 0.9645        |
| 0.1863        | 80.0  | 160  | 0.2762          | 0.4241   | 0.6830        | 0.9649           | nan                | 0.3756           | 0.9904             | 0.0           | 0.3079      | 0.9643        |
| 0.1656        | 85.0  | 170  | 0.2730          | 0.4241   | 0.6809        | 0.9653           | nan                | 0.3708           | 0.9911             | 0.0           | 0.3076      | 0.9648        |
| 0.1745        | 90.0  | 180  | 0.2740          | 0.4241   | 0.6821        | 0.9651           | nan                | 0.3736           | 0.9907             | 0.0           | 0.3079      | 0.9645        |
| 0.1726        | 95.0  | 190  | 0.2779          | 0.4242   | 0.6854        | 0.9645           | nan                | 0.3809           | 0.9898             | 0.0           | 0.3085      | 0.9639        |
| 0.158         | 100.0 | 200  | 0.2661          | 0.4248   | 0.6808        | 0.9656           | nan                | 0.3701           | 0.9915             | 0.0           | 0.3094      | 0.9651        |
| 0.19          | 105.0 | 210  | 0.2667          | 0.4240   | 0.6790        | 0.9656           | nan                | 0.3664           | 0.9916             | 0.0           | 0.3070      | 0.9651        |
| 0.1533        | 110.0 | 220  | 0.2696          | 0.4258   | 0.6843        | 0.9655           | nan                | 0.3777           | 0.9910             | 0.0           | 0.3126      | 0.9649        |
| 0.1644        | 115.0 | 230  | 0.2690          | 0.4261   | 0.6855        | 0.9654           | nan                | 0.3803           | 0.9908             | 0.0           | 0.3136      | 0.9648        |
| 0.1594        | 120.0 | 240  | 0.2636          | 0.4256   | 0.6832        | 0.9656           | nan                | 0.3752           | 0.9912             | 0.0           | 0.3118      | 0.9650        |


### Framework versions

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