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_7
  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_7

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: 0.1296
- Mean Iou: 0.6242
- Mean Accuracy: 0.6623
- Overall Accuracy: 0.9652
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.3319
- Accuracy Undropoff: 0.9926
- Iou Unlabeled: nan
- Iou Dropoff: 0.2838
- Iou Undropoff: 0.9647

## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.9278        | 5.0   | 10   | 0.8454          | 0.3197   | 0.5545        | 0.8788           | nan                | 0.2009           | 0.9082             | 0.0           | 0.0807      | 0.8785        |
| 0.5551        | 10.0  | 20   | 0.4668          | 0.3221   | 0.5042        | 0.9540           | nan                | 0.0135           | 0.9948             | 0.0           | 0.0122      | 0.9540        |
| 0.3667        | 15.0  | 30   | 0.3354          | 0.3218   | 0.5035        | 0.9570           | nan                | 0.0088           | 0.9982             | 0.0           | 0.0085      | 0.9570        |
| 0.2402        | 20.0  | 40   | 0.2678          | 0.5985   | 0.6492        | 0.9587           | nan                | 0.3116           | 0.9868             | nan           | 0.2388      | 0.9582        |
| 0.1562        | 25.0  | 50   | 0.2101          | 0.6240   | 0.6719        | 0.9631           | nan                | 0.3544           | 0.9895             | nan           | 0.2854      | 0.9625        |
| 0.1159        | 30.0  | 60   | 0.1704          | 0.6262   | 0.6641        | 0.9654           | nan                | 0.3353           | 0.9928             | nan           | 0.2875      | 0.9650        |
| 0.0869        | 35.0  | 70   | 0.1443          | 0.6380   | 0.6817        | 0.9657           | nan                | 0.3720           | 0.9915             | nan           | 0.3108      | 0.9652        |
| 0.079         | 40.0  | 80   | 0.1350          | 0.6072   | 0.6360        | 0.9654           | nan                | 0.2766           | 0.9953             | nan           | 0.2494      | 0.9650        |
| 0.0647        | 45.0  | 90   | 0.1370          | 0.5800   | 0.6031        | 0.9643           | nan                | 0.2090           | 0.9971             | nan           | 0.1959      | 0.9640        |
| 0.0587        | 50.0  | 100  | 0.1336          | 0.6276   | 0.6796        | 0.9628           | nan                | 0.3707           | 0.9885             | nan           | 0.2929      | 0.9622        |
| 0.0575        | 55.0  | 110  | 0.1313          | 0.6189   | 0.6531        | 0.9654           | nan                | 0.3126           | 0.9937             | nan           | 0.2729      | 0.9649        |
| 0.0527        | 60.0  | 120  | 0.1298          | 0.6252   | 0.6655        | 0.9648           | nan                | 0.3391           | 0.9920             | nan           | 0.2860      | 0.9643        |
| 0.0491        | 65.0  | 130  | 0.1313          | 0.6110   | 0.6492        | 0.9635           | nan                | 0.3063           | 0.9920             | nan           | 0.2589      | 0.9631        |
| 0.0441        | 70.0  | 140  | 0.1295          | 0.6103   | 0.6429        | 0.9648           | nan                | 0.2919           | 0.9939             | nan           | 0.2562      | 0.9643        |
| 0.0426        | 75.0  | 150  | 0.1233          | 0.6271   | 0.6633        | 0.9659           | nan                | 0.3333           | 0.9933             | nan           | 0.2887      | 0.9654        |
| 0.0477        | 80.0  | 160  | 0.1286          | 0.6255   | 0.6629        | 0.9655           | nan                | 0.3328           | 0.9929             | nan           | 0.2861      | 0.9650        |
| 0.039         | 85.0  | 170  | 0.1265          | 0.6380   | 0.6824        | 0.9656           | nan                | 0.3735           | 0.9913             | nan           | 0.3109      | 0.9650        |
| 0.0378        | 90.0  | 180  | 0.1309          | 0.6185   | 0.6543        | 0.9650           | nan                | 0.3154           | 0.9932             | nan           | 0.2725      | 0.9645        |
| 0.0362        | 95.0  | 190  | 0.1266          | 0.6311   | 0.6715        | 0.9655           | nan                | 0.3508           | 0.9922             | nan           | 0.2973      | 0.9650        |
| 0.0394        | 100.0 | 200  | 0.1307          | 0.6274   | 0.6635        | 0.9659           | nan                | 0.3337           | 0.9934             | nan           | 0.2894      | 0.9655        |
| 0.0362        | 105.0 | 210  | 0.1271          | 0.6366   | 0.6789        | 0.9658           | nan                | 0.3661           | 0.9918             | nan           | 0.3080      | 0.9653        |
| 0.0361        | 110.0 | 220  | 0.1274          | 0.6317   | 0.6736        | 0.9653           | nan                | 0.3554           | 0.9918             | nan           | 0.2987      | 0.9648        |
| 0.0353        | 115.0 | 230  | 0.1290          | 0.6216   | 0.6579        | 0.9652           | nan                | 0.3228           | 0.9931             | nan           | 0.2784      | 0.9647        |
| 0.0344        | 120.0 | 240  | 0.1296          | 0.6242   | 0.6623        | 0.9652           | nan                | 0.3319           | 0.9926             | nan           | 0.2838      | 0.9647        |


### Framework versions

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