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-RGB-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-RGB-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: 0.4848
- Mean Iou: 0.4257
- Mean Accuracy: 0.7972
- Overall Accuracy: 0.9466
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.6343
- Accuracy Undropoff: 0.9601
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.3321
- Iou Undropoff: 0.9451

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.0108        | 5.0   | 10   | 1.0721          | 0.1514   | 0.5401        | 0.4205           | nan                | 0.6706           | 0.4096             | 0.0           | 0.0494      | 0.4047        |
| 0.9654        | 10.0  | 20   | 0.9802          | 0.2190   | 0.6570        | 0.5944           | nan                | 0.7253           | 0.5887             | 0.0           | 0.0745      | 0.5826        |
| 0.9175        | 15.0  | 30   | 0.9047          | 0.2553   | 0.7350        | 0.6792           | nan                | 0.7960           | 0.6741             | 0.0           | 0.0973      | 0.6686        |
| 0.9052        | 20.0  | 40   | 0.8427          | 0.2812   | 0.7661        | 0.7377           | nan                | 0.7971           | 0.7351             | 0.0           | 0.1146      | 0.7290        |
| 0.8555        | 25.0  | 50   | 0.7970          | 0.3063   | 0.7827        | 0.7900           | nan                | 0.7748           | 0.7906             | 0.0           | 0.1357      | 0.7832        |
| 0.8291        | 30.0  | 60   | 0.7543          | 0.3289   | 0.7891        | 0.8332           | nan                | 0.7410           | 0.8372             | 0.0           | 0.1586      | 0.8282        |
| 0.7923        | 35.0  | 70   | 0.7327          | 0.3375   | 0.7961        | 0.8471           | nan                | 0.7405           | 0.8517             | 0.0           | 0.1701      | 0.8425        |
| 0.7724        | 40.0  | 80   | 0.6994          | 0.3529   | 0.7968        | 0.8719           | nan                | 0.7149           | 0.8787             | 0.0           | 0.1906      | 0.8682        |
| 0.7215        | 45.0  | 90   | 0.6675          | 0.3694   | 0.7935        | 0.8954           | nan                | 0.6824           | 0.9047             | 0.0           | 0.2157      | 0.8926        |
| 0.6907        | 50.0  | 100  | 0.6521          | 0.3742   | 0.7998        | 0.9000           | nan                | 0.6904           | 0.9091             | 0.0           | 0.2252      | 0.8973        |
| 0.6768        | 55.0  | 110  | 0.6260          | 0.3850   | 0.8022        | 0.9118           | nan                | 0.6827           | 0.9217             | 0.0           | 0.2455      | 0.9094        |
| 0.659         | 60.0  | 120  | 0.6010          | 0.3965   | 0.7973        | 0.9244           | nan                | 0.6586           | 0.9359             | 0.0           | 0.2671      | 0.9224        |
| 0.6265        | 65.0  | 130  | 0.5847          | 0.4005   | 0.7992        | 0.9276           | nan                | 0.6592           | 0.9393             | 0.0           | 0.2757      | 0.9258        |
| 0.6134        | 70.0  | 140  | 0.5673          | 0.4060   | 0.8022        | 0.9316           | nan                | 0.6611           | 0.9433             | 0.0           | 0.2881      | 0.9297        |
| 0.5864        | 75.0  | 150  | 0.5401          | 0.4132   | 0.7961        | 0.9383           | nan                | 0.6410           | 0.9511             | 0.0           | 0.3029      | 0.9366        |
| 0.5686        | 80.0  | 160  | 0.5289          | 0.4153   | 0.7974        | 0.9395           | nan                | 0.6424           | 0.9524             | 0.0           | 0.3080      | 0.9379        |
| 0.5597        | 85.0  | 170  | 0.5386          | 0.4114   | 0.8079        | 0.9350           | nan                | 0.6692           | 0.9465             | 0.0           | 0.3011      | 0.9331        |
| 0.5718        | 90.0  | 180  | 0.5080          | 0.4210   | 0.7947        | 0.9438           | nan                | 0.6321           | 0.9573             | 0.0           | 0.3208      | 0.9423        |
| 0.517         | 95.0  | 190  | 0.5026          | 0.4222   | 0.7956        | 0.9445           | nan                | 0.6332           | 0.9580             | 0.0           | 0.3236      | 0.9430        |
| 0.5252        | 100.0 | 200  | 0.4990          | 0.4232   | 0.7969        | 0.9450           | nan                | 0.6354           | 0.9584             | 0.0           | 0.3261      | 0.9435        |
| 0.5174        | 105.0 | 210  | 0.4951          | 0.4223   | 0.8012        | 0.9437           | nan                | 0.6457           | 0.9567             | 0.0           | 0.3249      | 0.9422        |
| 0.5217        | 110.0 | 220  | 0.4882          | 0.4238   | 0.7993        | 0.9450           | nan                | 0.6404           | 0.9582             | 0.0           | 0.3280      | 0.9435        |
| 0.5224        | 115.0 | 230  | 0.4846          | 0.4258   | 0.7968        | 0.9467           | nan                | 0.6333           | 0.9603             | 0.0           | 0.3321      | 0.9452        |
| 0.5399        | 120.0 | 240  | 0.4848          | 0.4257   | 0.7972        | 0.9466           | nan                | 0.6343           | 0.9601             | 0.0           | 0.3321      | 0.9451        |


### Framework versions

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