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_6
  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_6

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: 0.2315
- Mean Iou: 0.6980
- Mean Accuracy: 0.7503
- Overall Accuracy: 0.9714
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.5091
- Accuracy Undropoff: 0.9915
- Iou Unlabeled: nan
- Iou Dropoff: 0.4253
- Iou Undropoff: 0.9708

## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.0694        | 5.0   | 10   | 1.0190          | 0.2533   | 0.6371        | 0.6676           | nan                | 0.6038           | 0.6703             | 0.0           | 0.0976      | 0.6624        |
| 0.8457        | 10.0  | 20   | 0.7681          | 0.4126   | 0.7662        | 0.9307           | nan                | 0.5867           | 0.9457             | 0.0           | 0.3078      | 0.9300        |
| 0.6049        | 15.0  | 30   | 0.5718          | 0.4362   | 0.7527        | 0.9568           | nan                | 0.5301           | 0.9753             | 0.0           | 0.3527      | 0.9561        |
| 0.5206        | 20.0  | 40   | 0.4181          | 0.4522   | 0.7468        | 0.9662           | nan                | 0.5076           | 0.9861             | 0.0           | 0.3909      | 0.9656        |
| 0.3478        | 25.0  | 50   | 0.3144          | 0.4603   | 0.7376        | 0.9709           | nan                | 0.4832           | 0.9920             | 0.0           | 0.4105      | 0.9705        |
| 0.2023        | 30.0  | 60   | 0.2893          | 0.4654   | 0.7612        | 0.9701           | nan                | 0.5332           | 0.9891             | 0.0           | 0.4267      | 0.9695        |
| 0.1367        | 35.0  | 70   | 0.2351          | 0.6813   | 0.7176        | 0.9715           | nan                | 0.4407           | 0.9946             | nan           | 0.3916      | 0.9710        |
| 0.1272        | 40.0  | 80   | 0.2364          | 0.6824   | 0.7217        | 0.9713           | nan                | 0.4495           | 0.9939             | nan           | 0.3941      | 0.9707        |
| 0.0929        | 45.0  | 90   | 0.2536          | 0.4704   | 0.7617        | 0.9718           | nan                | 0.5326           | 0.9909             | 0.0           | 0.4401      | 0.9712        |
| 0.0756        | 50.0  | 100  | 0.2253          | 0.6950   | 0.7479        | 0.9710           | nan                | 0.5045           | 0.9912             | nan           | 0.4197      | 0.9704        |
| 0.0756        | 55.0  | 110  | 0.2305          | 0.7043   | 0.7606        | 0.9716           | nan                | 0.5305           | 0.9908             | nan           | 0.4375      | 0.9710        |
| 0.0721        | 60.0  | 120  | 0.2213          | 0.6964   | 0.7448        | 0.9716           | nan                | 0.4974           | 0.9922             | nan           | 0.4218      | 0.9711        |
| 0.0683        | 65.0  | 130  | 0.2338          | 0.7047   | 0.7631        | 0.9715           | nan                | 0.5359           | 0.9904             | nan           | 0.4385      | 0.9708        |
| 0.0642        | 70.0  | 140  | 0.2314          | 0.7046   | 0.7637        | 0.9714           | nan                | 0.5373           | 0.9902             | nan           | 0.4385      | 0.9707        |
| 0.0623        | 75.0  | 150  | 0.2205          | 0.7013   | 0.7565        | 0.9714           | nan                | 0.5222           | 0.9909             | nan           | 0.4317      | 0.9708        |
| 0.0601        | 80.0  | 160  | 0.2209          | 0.6983   | 0.7496        | 0.9715           | nan                | 0.5075           | 0.9917             | nan           | 0.4257      | 0.9709        |
| 0.0557        | 85.0  | 170  | 0.2067          | 0.6982   | 0.7463        | 0.9719           | nan                | 0.5003           | 0.9923             | nan           | 0.4252      | 0.9713        |
| 0.0571        | 90.0  | 180  | 0.2354          | 0.7022   | 0.7603        | 0.9712           | nan                | 0.5302           | 0.9904             | nan           | 0.4339      | 0.9706        |
| 0.0544        | 95.0  | 190  | 0.2240          | 0.7010   | 0.7562        | 0.9714           | nan                | 0.5215           | 0.9909             | nan           | 0.4311      | 0.9708        |
| 0.0553        | 100.0 | 200  | 0.2204          | 0.6968   | 0.7454        | 0.9717           | nan                | 0.4987           | 0.9922             | nan           | 0.4225      | 0.9711        |
| 0.0525        | 105.0 | 210  | 0.2332          | 0.7050   | 0.7625        | 0.9716           | nan                | 0.5344           | 0.9906             | nan           | 0.4390      | 0.9710        |
| 0.0524        | 110.0 | 220  | 0.2371          | 0.7033   | 0.7605        | 0.9715           | nan                | 0.5304           | 0.9906             | nan           | 0.4359      | 0.9708        |
| 0.0513        | 115.0 | 230  | 0.2333          | 0.6987   | 0.7519        | 0.9714           | nan                | 0.5125           | 0.9913             | nan           | 0.4267      | 0.9707        |
| 0.0537        | 120.0 | 240  | 0.2315          | 0.6980   | 0.7503        | 0.9714           | nan                | 0.5091           | 0.9915             | nan           | 0.4253      | 0.9708        |


### Framework versions

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