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_3
  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_3

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: 0.2768
- Mean Iou: 0.3194
- Mean Accuracy: 0.4999
- Overall Accuracy: 0.9578
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.0006
- Accuracy Undropoff: 0.9993
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.0006
- Iou Undropoff: 0.9578

## 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-06
- train_batch_size: 15
- eval_batch_size: 15
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.0992        | 5.0   | 10   | 1.0599          | 0.1938   | 0.4241        | 0.5281           | nan                | 0.3106           | 0.5376             | 0.0           | 0.0540      | 0.5273        |
| 1.0188        | 10.0  | 20   | 0.9493          | 0.2781   | 0.4808        | 0.7846           | nan                | 0.1494           | 0.8122             | 0.0           | 0.0476      | 0.7868        |
| 0.9218        | 15.0  | 30   | 0.8130          | 0.3074   | 0.4913        | 0.8851           | nan                | 0.0618           | 0.9209             | 0.0           | 0.0364      | 0.8858        |
| 0.8411        | 20.0  | 40   | 0.7253          | 0.3089   | 0.4866        | 0.9038           | nan                | 0.0315           | 0.9416             | 0.0           | 0.0221      | 0.9047        |
| 0.7583        | 25.0  | 50   | 0.6719          | 0.3097   | 0.4890        | 0.9069           | nan                | 0.0331           | 0.9448             | 0.0           | 0.0216      | 0.9076        |
| 0.688         | 30.0  | 60   | 0.6303          | 0.3109   | 0.4883        | 0.9170           | nan                | 0.0207           | 0.9559             | 0.0           | 0.0149      | 0.9179        |
| 0.6279        | 35.0  | 70   | 0.5919          | 0.3139   | 0.4918        | 0.9276           | nan                | 0.0164           | 0.9671             | 0.0           | 0.0133      | 0.9283        |
| 0.5533        | 40.0  | 80   | 0.5375          | 0.3168   | 0.4961        | 0.9377           | nan                | 0.0144           | 0.9777             | 0.0           | 0.0125      | 0.9380        |
| 0.5116        | 45.0  | 90   | 0.5111          | 0.3176   | 0.4970        | 0.9442           | nan                | 0.0093           | 0.9847             | 0.0           | 0.0083      | 0.9445        |
| 0.4801        | 50.0  | 100  | 0.4696          | 0.3183   | 0.4981        | 0.9492           | nan                | 0.0062           | 0.9901             | 0.0           | 0.0057      | 0.9492        |
| 0.4744        | 55.0  | 110  | 0.4317          | 0.3187   | 0.4987        | 0.9543           | nan                | 0.0018           | 0.9956             | 0.0           | 0.0017      | 0.9543        |
| 0.4494        | 60.0  | 120  | 0.3991          | 0.3189   | 0.4991        | 0.9555           | nan                | 0.0013           | 0.9969             | 0.0           | 0.0012      | 0.9555        |
| 0.386         | 65.0  | 130  | 0.3737          | 0.3189   | 0.4990        | 0.9565           | nan                | 0.0000           | 0.9980             | 0.0           | 0.0000      | 0.9565        |
| 0.3674        | 70.0  | 140  | 0.3538          | 0.3191   | 0.4994        | 0.9567           | nan                | 0.0007           | 0.9981             | 0.0           | 0.0007      | 0.9567        |
| 0.3601        | 75.0  | 150  | 0.3413          | 0.3192   | 0.4995        | 0.9573           | nan                | 0.0002           | 0.9988             | 0.0           | 0.0002      | 0.9573        |
| 0.3626        | 80.0  | 160  | 0.3225          | 0.3193   | 0.4996        | 0.9569           | nan                | 0.0009           | 0.9984             | 0.0           | 0.0009      | 0.9569        |
| 0.3331        | 85.0  | 170  | 0.3163          | 0.3195   | 0.5000        | 0.9576           | nan                | 0.0009           | 0.9991             | 0.0           | 0.0009      | 0.9576        |
| 0.319         | 90.0  | 180  | 0.3004          | 0.3200   | 0.5008        | 0.9577           | nan                | 0.0024           | 0.9991             | 0.0           | 0.0024      | 0.9577        |
| 0.3163        | 95.0  | 190  | 0.2931          | 0.3198   | 0.5004        | 0.9575           | nan                | 0.0020           | 0.9989             | 0.0           | 0.0020      | 0.9575        |
| 0.3185        | 100.0 | 200  | 0.2920          | 0.3194   | 0.4999        | 0.9577           | nan                | 0.0006           | 0.9992             | 0.0           | 0.0006      | 0.9577        |
| 0.3122        | 105.0 | 210  | 0.2831          | 0.3194   | 0.4999        | 0.9578           | nan                | 0.0005           | 0.9994             | 0.0           | 0.0005      | 0.9578        |
| 0.3218        | 110.0 | 220  | 0.2788          | 0.3195   | 0.5000        | 0.9576           | nan                | 0.0009           | 0.9991             | 0.0           | 0.0009      | 0.9576        |
| 0.3037        | 115.0 | 230  | 0.2752          | 0.3194   | 0.4999        | 0.9577           | nan                | 0.0006           | 0.9992             | 0.0           | 0.0006      | 0.9577        |
| 0.3319        | 120.0 | 240  | 0.2768          | 0.3194   | 0.4999        | 0.9578           | nan                | 0.0006           | 0.9993             | 0.0           | 0.0006      | 0.9578        |


### Framework versions

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