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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: custom-object-test4
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. -->
# custom-object-test4
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sungile/custom-object-masking5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2992
- Mean Iou: 0.0
- Mean Accuracy: nan
- Overall Accuracy: nan
- Accuracy Unknown: nan
- Accuracy Background: nan
- Accuracy Object: nan
- Iou Unknown: 0.0
- Iou Background: 0.0
- Iou Object: 0.0
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unknown | Accuracy Background | Accuracy Object | Iou Unknown | Iou Background | Iou Object |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:|
| 1.0218 | 0.25 | 20 | 1.0125 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.8291 | 0.5 | 40 | 0.7527 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.7097 | 0.75 | 60 | 0.6208 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.606 | 1.0 | 80 | 0.5042 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.5577 | 1.25 | 100 | 0.4111 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.4422 | 1.5 | 120 | 0.4041 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.3799 | 1.75 | 140 | 0.3846 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.3578 | 2.0 | 160 | 0.3197 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | nan |
| 0.3401 | 2.25 | 180 | 0.3423 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | nan |
| 0.3223 | 2.5 | 200 | 0.3077 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.3068 | 2.75 | 220 | 0.3110 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
| 0.2823 | 3.0 | 240 | 0.2992 | 0.0 | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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