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_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-RGBD-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: 0.3428
- Mean Iou: 0.4792
- Mean Accuracy: 0.5000
- Overall Accuracy: 0.9583
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.0001
- Accuracy Undropoff: 0.9999
- Iou Unlabeled: nan
- Iou Dropoff: 0.0001
- Iou Undropoff: 0.9583

## 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: 3e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.8047        | 5.0   | 10   | 0.9867          | 0.2744   | 0.6315        | 0.7475           | nan                | 0.5049           | 0.7581             | 0.0           | 0.0812      | 0.7422        |
| 0.7528        | 10.0  | 20   | 0.8526          | 0.3461   | 0.5957        | 0.9213           | nan                | 0.2406           | 0.9508             | 0.0           | 0.1178      | 0.9205        |
| 0.7087        | 15.0  | 30   | 0.7023          | 0.3450   | 0.5533        | 0.9467           | nan                | 0.1243           | 0.9824             | 0.0           | 0.0887      | 0.9464        |
| 0.6601        | 20.0  | 40   | 0.6251          | 0.3381   | 0.5390        | 0.9462           | nan                | 0.0948           | 0.9832             | 0.0           | 0.0684      | 0.9460        |
| 0.6274        | 25.0  | 50   | 0.5828          | 0.3286   | 0.5178        | 0.9486           | nan                | 0.0479           | 0.9876             | 0.0           | 0.0374      | 0.9485        |
| 0.5929        | 30.0  | 60   | 0.5478          | 0.3257   | 0.5122        | 0.9488           | nan                | 0.0359           | 0.9884             | 0.0           | 0.0284      | 0.9487        |
| 0.5672        | 35.0  | 70   | 0.5237          | 0.3240   | 0.5088        | 0.9494           | nan                | 0.0283           | 0.9893             | 0.0           | 0.0227      | 0.9493        |
| 0.5454        | 40.0  | 80   | 0.4966          | 0.4856   | 0.5072        | 0.9529           | nan                | 0.0212           | 0.9933             | nan           | 0.0183      | 0.9528        |
| 0.5261        | 45.0  | 90   | 0.4700          | 0.3234   | 0.5062        | 0.9553           | nan                | 0.0163           | 0.9960             | 0.0           | 0.0149      | 0.9552        |
| 0.5012        | 50.0  | 100  | 0.4576          | 0.4832   | 0.5041        | 0.9563           | nan                | 0.0107           | 0.9974             | nan           | 0.0101      | 0.9563        |
| 0.4875        | 55.0  | 110  | 0.4430          | 0.4811   | 0.5018        | 0.9566           | nan                | 0.0058           | 0.9978             | nan           | 0.0056      | 0.9565        |
| 0.4622        | 60.0  | 120  | 0.4328          | 0.4800   | 0.5007        | 0.9570           | nan                | 0.0031           | 0.9983             | nan           | 0.0030      | 0.9570        |
| 0.4394        | 65.0  | 130  | 0.4179          | 0.4796   | 0.5004        | 0.9572           | nan                | 0.0021           | 0.9986             | nan           | 0.0021      | 0.9572        |
| 0.4352        | 70.0  | 140  | 0.4048          | 0.4795   | 0.5002        | 0.9573           | nan                | 0.0016           | 0.9988             | nan           | 0.0016      | 0.9573        |
| 0.426         | 75.0  | 150  | 0.3881          | 0.4796   | 0.5003        | 0.9577           | nan                | 0.0015           | 0.9992             | nan           | 0.0014      | 0.9577        |
| 0.4175        | 80.0  | 160  | 0.3794          | 0.4797   | 0.5004        | 0.9579           | nan                | 0.0014           | 0.9994             | nan           | 0.0014      | 0.9579        |
| 0.4087        | 85.0  | 170  | 0.3742          | 0.3196   | 0.5002        | 0.9577           | nan                | 0.0012           | 0.9992             | 0.0           | 0.0012      | 0.9577        |
| 0.3887        | 90.0  | 180  | 0.3645          | 0.4792   | 0.4999        | 0.9581           | nan                | 0.0003           | 0.9996             | nan           | 0.0003      | 0.9581        |
| 0.3799        | 95.0  | 190  | 0.3540          | 0.4791   | 0.4999        | 0.9581           | nan                | 0.0001           | 0.9997             | nan           | 0.0001      | 0.9581        |
| 0.376         | 100.0 | 200  | 0.3511          | 0.4792   | 0.4999        | 0.9582           | nan                | 0.0001           | 0.9998             | nan           | 0.0001      | 0.9582        |
| 0.3677        | 105.0 | 210  | 0.3452          | 0.4792   | 0.4999        | 0.9582           | nan                | 0.0001           | 0.9998             | nan           | 0.0001      | 0.9582        |
| 0.358         | 110.0 | 220  | 0.3437          | 0.4792   | 0.4999        | 0.9582           | nan                | 0.0001           | 0.9998             | nan           | 0.0001      | 0.9582        |
| 0.3997        | 115.0 | 230  | 0.3434          | 0.4792   | 0.5000        | 0.9583           | nan                | 0.0001           | 0.9999             | nan           | 0.0001      | 0.9583        |
| 0.3769        | 120.0 | 240  | 0.3428          | 0.4792   | 0.5000        | 0.9583           | nan                | 0.0001           | 0.9999             | nan           | 0.0001      | 0.9583        |


### Framework versions

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