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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGB-b5_7
  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_7

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: 0.1841
- Mean Iou: 0.7025
- Mean Accuracy: 0.7532
- Overall Accuracy: 0.9721
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.5145
- Accuracy Undropoff: 0.9919
- Iou Unlabeled: nan
- Iou Dropoff: 0.4336
- Iou Undropoff: 0.9715

## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 0.8255        | 5.0   | 10   | 0.7949          | 0.4128   | 0.7856        | 0.9393           | nan                | 0.6179           | 0.9533             | 0.0           | 0.3007      | 0.9377        |
| 0.4434        | 10.0  | 20   | 0.4247          | 0.4471   | 0.7066        | 0.9705           | nan                | 0.4187           | 0.9944             | 0.0           | 0.3714      | 0.9700        |
| 0.2107        | 15.0  | 30   | 0.2726          | 0.6711   | 0.7003        | 0.9715           | nan                | 0.4046           | 0.9961             | nan           | 0.3713      | 0.9710        |
| 0.1678        | 20.0  | 40   | 0.2388          | 0.6801   | 0.7343        | 0.9691           | nan                | 0.4782           | 0.9904             | nan           | 0.3917      | 0.9685        |
| 0.0972        | 25.0  | 50   | 0.1849          | 0.6764   | 0.7096        | 0.9715           | nan                | 0.4241           | 0.9952             | nan           | 0.3818      | 0.9709        |
| 0.0604        | 30.0  | 60   | 0.2019          | 0.4644   | 0.7568        | 0.9704           | nan                | 0.5239           | 0.9897             | 0.0           | 0.4236      | 0.9697        |
| 0.0497        | 35.0  | 70   | 0.1793          | 0.6838   | 0.7345        | 0.9700           | nan                | 0.4775           | 0.9914             | nan           | 0.3983      | 0.9694        |
| 0.0492        | 40.0  | 80   | 0.2000          | 0.4639   | 0.7567        | 0.9702           | nan                | 0.5239           | 0.9896             | 0.0           | 0.4223      | 0.9695        |
| 0.0409        | 45.0  | 90   | 0.1893          | 0.7030   | 0.7778        | 0.9696           | nan                | 0.5687           | 0.9869             | nan           | 0.4372      | 0.9688        |
| 0.0328        | 50.0  | 100  | 0.1842          | 0.7040   | 0.7715        | 0.9704           | nan                | 0.5545           | 0.9885             | nan           | 0.4382      | 0.9697        |
| 0.0332        | 55.0  | 110  | 0.1781          | 0.7015   | 0.7563        | 0.9715           | nan                | 0.5216           | 0.9910             | nan           | 0.4322      | 0.9709        |
| 0.0314        | 60.0  | 120  | 0.1732          | 0.6890   | 0.7305        | 0.9717           | nan                | 0.4675           | 0.9935             | nan           | 0.4068      | 0.9711        |
| 0.0318        | 65.0  | 130  | 0.1786          | 0.6971   | 0.7477        | 0.9715           | nan                | 0.5037           | 0.9918             | nan           | 0.4233      | 0.9709        |
| 0.0291        | 70.0  | 140  | 0.1814          | 0.7119   | 0.7687        | 0.9725           | nan                | 0.5466           | 0.9909             | nan           | 0.4521      | 0.9718        |
| 0.0273        | 75.0  | 150  | 0.1755          | 0.7101   | 0.7677        | 0.9722           | nan                | 0.5446           | 0.9907             | nan           | 0.4487      | 0.9715        |
| 0.0274        | 80.0  | 160  | 0.1786          | 0.7006   | 0.7494        | 0.9720           | nan                | 0.5066           | 0.9922             | nan           | 0.4297      | 0.9714        |
| 0.0248        | 85.0  | 170  | 0.1741          | 0.7029   | 0.7526        | 0.9722           | nan                | 0.5131           | 0.9921             | nan           | 0.4341      | 0.9716        |
| 0.0248        | 90.0  | 180  | 0.1832          | 0.7050   | 0.7595        | 0.9719           | nan                | 0.5278           | 0.9912             | nan           | 0.4387      | 0.9713        |
| 0.0242        | 95.0  | 190  | 0.1808          | 0.7028   | 0.7539        | 0.9720           | nan                | 0.5160           | 0.9918             | nan           | 0.4341      | 0.9714        |
| 0.024         | 100.0 | 200  | 0.1796          | 0.7022   | 0.7501        | 0.9723           | nan                | 0.5077           | 0.9925             | nan           | 0.4327      | 0.9717        |
| 0.0231        | 105.0 | 210  | 0.1835          | 0.7137   | 0.7731        | 0.9724           | nan                | 0.5557           | 0.9905             | nan           | 0.4556      | 0.9717        |
| 0.0238        | 110.0 | 220  | 0.1823          | 0.7046   | 0.7565        | 0.9721           | nan                | 0.5214           | 0.9917             | nan           | 0.4376      | 0.9715        |
| 0.0228        | 115.0 | 230  | 0.1833          | 0.7009   | 0.7504        | 0.9720           | nan                | 0.5088           | 0.9921             | nan           | 0.4305      | 0.9714        |
| 0.0255        | 120.0 | 240  | 0.1841          | 0.7025   | 0.7532        | 0.9721           | nan                | 0.5145           | 0.9919             | nan           | 0.4336      | 0.9715        |


### Framework versions

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