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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dropoff-utcustom-train-SF-RGBD-b0_1
  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-b0_1

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: 0.4979
- Mean Iou: 0.4170
- Mean Accuracy: 0.6846
- Overall Accuracy: 0.9603
- Accuracy Unlabeled: nan
- Accuracy Dropoff: 0.3839
- Accuracy Undropoff: 0.9853
- Iou Unlabeled: 0.0
- Iou Dropoff: 0.2914
- Iou Undropoff: 0.9597

## 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
| 1.0495        | 5.0   | 10   | 1.0890          | 0.1852   | 0.3572        | 0.4990           | nan                | 0.2026           | 0.5119             | 0.0           | 0.0474      | 0.5081        |
| 0.9941        | 10.0  | 20   | 1.0479          | 0.3452   | 0.8357        | 0.8479           | nan                | 0.8225           | 0.8490             | 0.0           | 0.1931      | 0.8425        |
| 0.9448        | 15.0  | 30   | 0.9839          | 0.3790   | 0.8217        | 0.9010           | nan                | 0.7351           | 0.9082             | 0.0           | 0.2390      | 0.8980        |
| 0.8912        | 20.0  | 40   | 0.9041          | 0.3845   | 0.7150        | 0.9247           | nan                | 0.4863           | 0.9437             | 0.0           | 0.2303      | 0.9233        |
| 0.8458        | 25.0  | 50   | 0.7997          | 0.3835   | 0.6687        | 0.9326           | nan                | 0.3808           | 0.9565             | 0.0           | 0.2188      | 0.9316        |
| 0.8299        | 30.0  | 60   | 0.7387          | 0.3751   | 0.6333        | 0.9326           | nan                | 0.3068           | 0.9597             | 0.0           | 0.1934      | 0.9318        |
| 0.7518        | 35.0  | 70   | 0.6810          | 0.3791   | 0.6322        | 0.9404           | nan                | 0.2961           | 0.9683             | 0.0           | 0.1975      | 0.9397        |
| 0.6943        | 40.0  | 80   | 0.6322          | 0.3703   | 0.6069        | 0.9422           | nan                | 0.2411           | 0.9726             | 0.0           | 0.1691      | 0.9417        |
| 0.6617        | 45.0  | 90   | 0.6071          | 0.3780   | 0.6240        | 0.9454           | nan                | 0.2734           | 0.9746             | 0.0           | 0.1892      | 0.9449        |
| 0.634         | 50.0  | 100  | 0.5932          | 0.3765   | 0.6106        | 0.9497           | nan                | 0.2407           | 0.9805             | 0.0           | 0.1802      | 0.9494        |
| 0.6157        | 55.0  | 110  | 0.5829          | 0.3982   | 0.6538        | 0.9524           | nan                | 0.3281           | 0.9795             | 0.0           | 0.2425      | 0.9520        |
| 0.5814        | 60.0  | 120  | 0.5708          | 0.4038   | 0.6699        | 0.9533           | nan                | 0.3608           | 0.9790             | 0.0           | 0.2586      | 0.9528        |
| 0.5988        | 65.0  | 130  | 0.5575          | 0.3974   | 0.6456        | 0.9569           | nan                | 0.3061           | 0.9851             | 0.0           | 0.2357      | 0.9564        |
| 0.5583        | 70.0  | 140  | 0.5530          | 0.4224   | 0.7075        | 0.9576           | nan                | 0.4346           | 0.9803             | 0.0           | 0.3103      | 0.9570        |
| 0.5596        | 75.0  | 150  | 0.5264          | 0.4034   | 0.6522        | 0.9598           | nan                | 0.3167           | 0.9877             | 0.0           | 0.2510      | 0.9593        |
| 0.5524        | 80.0  | 160  | 0.5392          | 0.4208   | 0.7109        | 0.9567           | nan                | 0.4429           | 0.9790             | 0.0           | 0.3065      | 0.9560        |
| 0.5294        | 85.0  | 170  | 0.5257          | 0.4161   | 0.6913        | 0.9582           | nan                | 0.4002           | 0.9824             | 0.0           | 0.2909      | 0.9576        |
| 0.5477        | 90.0  | 180  | 0.5178          | 0.4207   | 0.6962        | 0.9591           | nan                | 0.4095           | 0.9829             | 0.0           | 0.3035      | 0.9584        |
| 0.528         | 95.0  | 190  | 0.5185          | 0.4183   | 0.6939        | 0.9590           | nan                | 0.4047           | 0.9831             | 0.0           | 0.2965      | 0.9584        |
| 0.5144        | 100.0 | 200  | 0.5004          | 0.4153   | 0.6788        | 0.9604           | nan                | 0.3716           | 0.9860             | 0.0           | 0.2859      | 0.9599        |
| 0.5313        | 105.0 | 210  | 0.5032          | 0.4199   | 0.7005        | 0.9585           | nan                | 0.4191           | 0.9819             | 0.0           | 0.3020      | 0.9578        |
| 0.5172        | 110.0 | 220  | 0.4993          | 0.4188   | 0.6931        | 0.9591           | nan                | 0.4030           | 0.9832             | 0.0           | 0.2978      | 0.9585        |
| 0.5124        | 115.0 | 230  | 0.4999          | 0.4167   | 0.6828        | 0.9606           | nan                | 0.3799           | 0.9858             | 0.0           | 0.2901      | 0.9600        |
| 0.5025        | 120.0 | 240  | 0.4979          | 0.4170   | 0.6846        | 0.9603           | nan                | 0.3839           | 0.9853             | 0.0           | 0.2914      | 0.9597        |


### Framework versions

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