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-b0_7
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# dropoff-utcustom-train-SF-RGBD-b0_7

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2075
- Mean Iou: 0.6372
- Mean Accuracy: 0.6861
- Overall Accuracy: 0.9647
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.3822
- Accuracy Undropoff: 0.9900
- Iou Unlabeled: nan
- Iou Dropoff: 0.3104
- Iou Undropoff: 0.9641

## 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: 8e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.9508        | 5.0   | 10   | 1.0263          | 0.3104   | 0.5474        | 0.8717           | nan                | 0.1937           | 0.9011             | 0.0           | 0.0605      | 0.8706        |
| 0.7814        | 10.0  | 20   | 0.7568          | 0.4971   | 0.5339        | 0.9361           | nan                | 0.0952           | 0.9726             | nan           | 0.0584      | 0.9359        |
| 0.642         | 15.0  | 30   | 0.5907          | 0.5134   | 0.5443        | 0.9494           | nan                | 0.1026           | 0.9861             | nan           | 0.0777      | 0.9492        |
| 0.5118        | 20.0  | 40   | 0.4804          | 0.3658   | 0.5923        | 0.9513           | nan                | 0.2006           | 0.9839             | 0.0           | 0.1464      | 0.9509        |
| 0.4581        | 25.0  | 50   | 0.4405          | 0.3715   | 0.5915        | 0.9569           | nan                | 0.1930           | 0.9900             | 0.0           | 0.1578      | 0.9565        |
| 0.4213        | 30.0  | 60   | 0.4146          | 0.3828   | 0.6136        | 0.9580           | nan                | 0.2379           | 0.9892             | 0.0           | 0.1910      | 0.9575        |
| 0.3571        | 35.0  | 70   | 0.3750          | 0.3846   | 0.6180        | 0.9578           | nan                | 0.2474           | 0.9887             | 0.0           | 0.1963      | 0.9574        |
| 0.3205        | 40.0  | 80   | 0.3478          | 0.5777   | 0.6202        | 0.9576           | nan                | 0.2522           | 0.9882             | nan           | 0.1982      | 0.9571        |
| 0.3114        | 45.0  | 90   | 0.3461          | 0.3895   | 0.6423        | 0.9541           | nan                | 0.3022           | 0.9824             | 0.0           | 0.2150      | 0.9535        |
| 0.2747        | 50.0  | 100  | 0.3253          | 0.5875   | 0.6357        | 0.9575           | nan                | 0.2847           | 0.9867             | nan           | 0.2180      | 0.9570        |
| 0.2593        | 55.0  | 110  | 0.3083          | 0.5967   | 0.6599        | 0.9552           | nan                | 0.3377           | 0.9820             | nan           | 0.2387      | 0.9546        |
| 0.2293        | 60.0  | 120  | 0.2762          | 0.5966   | 0.6389        | 0.9606           | nan                | 0.2880           | 0.9898             | nan           | 0.2331      | 0.9601        |
| 0.2306        | 65.0  | 130  | 0.2655          | 0.6016   | 0.6587        | 0.9577           | nan                | 0.3326           | 0.9848             | nan           | 0.2462      | 0.9571        |
| 0.2118        | 70.0  | 140  | 0.2446          | 0.6039   | 0.6509        | 0.9605           | nan                | 0.3133           | 0.9886             | nan           | 0.2479      | 0.9600        |
| 0.2038        | 75.0  | 150  | 0.2395          | 0.6164   | 0.6708        | 0.9607           | nan                | 0.3547           | 0.9870             | nan           | 0.2727      | 0.9601        |
| 0.1895        | 80.0  | 160  | 0.2196          | 0.6254   | 0.6721        | 0.9636           | nan                | 0.3542           | 0.9900             | nan           | 0.2878      | 0.9630        |
| 0.1681        | 85.0  | 170  | 0.2176          | 0.6302   | 0.6829        | 0.9630           | nan                | 0.3773           | 0.9884             | nan           | 0.2979      | 0.9624        |
| 0.1612        | 90.0  | 180  | 0.2175          | 0.6334   | 0.6870        | 0.9633           | nan                | 0.3857           | 0.9884             | nan           | 0.3042      | 0.9627        |
| 0.1545        | 95.0  | 190  | 0.2140          | 0.6337   | 0.6816        | 0.9644           | nan                | 0.3732           | 0.9900             | nan           | 0.3035      | 0.9638        |
| 0.1551        | 100.0 | 200  | 0.2134          | 0.6357   | 0.6891        | 0.9637           | nan                | 0.3896           | 0.9886             | nan           | 0.3083      | 0.9631        |
| 0.1508        | 105.0 | 210  | 0.2090          | 0.6359   | 0.6865        | 0.9642           | nan                | 0.3837           | 0.9894             | nan           | 0.3083      | 0.9636        |
| 0.1536        | 110.0 | 220  | 0.2057          | 0.6346   | 0.6801        | 0.9650           | nan                | 0.3694           | 0.9908             | nan           | 0.3048      | 0.9644        |
| 0.1392        | 115.0 | 230  | 0.2083          | 0.6387   | 0.6890        | 0.9646           | nan                | 0.3883           | 0.9896             | nan           | 0.3133      | 0.9640        |
| 0.1446        | 120.0 | 240  | 0.2075          | 0.6372   | 0.6861        | 0.9647           | nan                | 0.3822           | 0.9900             | nan           | 0.3104      | 0.9641        |


### Framework versions

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