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_2
  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_2

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: 0.4198
- Mean Iou: 0.3194
- Mean Accuracy: 0.4998
- Overall Accuracy: 0.9558
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.0023
- Accuracy Undropoff: 0.9972
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.0022
- Iou Undropoff: 0.9558

## 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: 4e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.989         | 5.0   | 10   | 1.0190          | 0.2162   | 0.5831        | 0.5879           | nan                | 0.5779           | 0.5883             | 0.0           | 0.0657      | 0.5829        |
| 0.9092        | 10.0  | 20   | 0.8686          | 0.3164   | 0.5199        | 0.8922           | nan                | 0.1137           | 0.9260             | 0.0           | 0.0539      | 0.8953        |
| 0.8483        | 15.0  | 30   | 0.7438          | 0.3256   | 0.5234        | 0.9219           | nan                | 0.0888           | 0.9581             | 0.0           | 0.0545      | 0.9224        |
| 0.7856        | 20.0  | 40   | 0.6571          | 0.3182   | 0.5013        | 0.9336           | nan                | 0.0297           | 0.9728             | 0.0           | 0.0210      | 0.9335        |
| 0.7459        | 25.0  | 50   | 0.6144          | 0.3164   | 0.4980        | 0.9324           | nan                | 0.0242           | 0.9718             | 0.0           | 0.0168      | 0.9324        |
| 0.7027        | 30.0  | 60   | 0.5861          | 0.3168   | 0.4975        | 0.9351           | nan                | 0.0202           | 0.9748             | 0.0           | 0.0151      | 0.9353        |
| 0.6827        | 35.0  | 70   | 0.5568          | 0.3171   | 0.4975        | 0.9391           | nan                | 0.0159           | 0.9791             | 0.0           | 0.0122      | 0.9391        |
| 0.6362        | 40.0  | 80   | 0.5405          | 0.3179   | 0.4982        | 0.9424           | nan                | 0.0138           | 0.9827             | 0.0           | 0.0112      | 0.9425        |
| 0.6098        | 45.0  | 90   | 0.5192          | 0.3174   | 0.4971        | 0.9449           | nan                | 0.0087           | 0.9855             | 0.0           | 0.0073      | 0.9449        |
| 0.5946        | 50.0  | 100  | 0.5025          | 0.3179   | 0.4978        | 0.9475           | nan                | 0.0072           | 0.9883             | 0.0           | 0.0062      | 0.9477        |
| 0.5868        | 55.0  | 110  | 0.4943          | 0.3179   | 0.4976        | 0.9490           | nan                | 0.0052           | 0.9900             | 0.0           | 0.0046      | 0.9491        |
| 0.5557        | 60.0  | 120  | 0.4798          | 0.3184   | 0.4983        | 0.9505           | nan                | 0.0051           | 0.9915             | 0.0           | 0.0045      | 0.9506        |
| 0.5327        | 65.0  | 130  | 0.4736          | 0.3184   | 0.4983        | 0.9514           | nan                | 0.0041           | 0.9925             | 0.0           | 0.0038      | 0.9514        |
| 0.525         | 70.0  | 140  | 0.4657          | 0.3187   | 0.4987        | 0.9526           | nan                | 0.0038           | 0.9937             | 0.0           | 0.0035      | 0.9526        |
| 0.5266        | 75.0  | 150  | 0.4528          | 0.3190   | 0.4992        | 0.9534           | nan                | 0.0037           | 0.9946             | 0.0           | 0.0034      | 0.9535        |
| 0.5139        | 80.0  | 160  | 0.4538          | 0.3189   | 0.4991        | 0.9533           | nan                | 0.0037           | 0.9945             | 0.0           | 0.0035      | 0.9534        |
| 0.5128        | 85.0  | 170  | 0.4460          | 0.3192   | 0.4995        | 0.9543           | nan                | 0.0033           | 0.9956             | 0.0           | 0.0031      | 0.9543        |
| 0.4901        | 90.0  | 180  | 0.4371          | 0.3192   | 0.4995        | 0.9548           | nan                | 0.0029           | 0.9961             | 0.0           | 0.0027      | 0.9548        |
| 0.4767        | 95.0  | 190  | 0.4325          | 0.3193   | 0.4997        | 0.9552           | nan                | 0.0029           | 0.9965             | 0.0           | 0.0027      | 0.9552        |
| 0.4692        | 100.0 | 200  | 0.4272          | 0.3193   | 0.4997        | 0.9556           | nan                | 0.0024           | 0.9970             | 0.0           | 0.0023      | 0.9556        |
| 0.4632        | 105.0 | 210  | 0.4251          | 0.3193   | 0.4996        | 0.9556           | nan                | 0.0023           | 0.9969             | 0.0           | 0.0023      | 0.9556        |
| 0.4626        | 110.0 | 220  | 0.4236          | 0.3193   | 0.4997        | 0.9556           | nan                | 0.0024           | 0.9970             | 0.0           | 0.0024      | 0.9556        |
| 0.4837        | 115.0 | 230  | 0.4216          | 0.3194   | 0.4998        | 0.9558           | nan                | 0.0023           | 0.9972             | 0.0           | 0.0023      | 0.9558        |
| 0.4809        | 120.0 | 240  | 0.4198          | 0.3194   | 0.4998        | 0.9558           | nan                | 0.0023           | 0.9972             | 0.0           | 0.0022      | 0.9558        |


### Framework versions

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