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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-test6
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-test6
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: 0.3457
- Mean Iou: 0.3368
- Mean Accuracy: 0.6736
- Overall Accuracy: 0.6736
- Accuracy Unknown: nan
- Accuracy Background: 0.6736
- Accuracy Object: nan
- Iou Unknown: 0.0
- Iou Background: 0.6736
- Iou Object: nan
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:|
| 0.7667 | 0.25 | 20 | 1.0272 | 0.3026 | 0.9079 | 0.9079 | nan | 0.9079 | nan | 0.0 | 0.9079 | 0.0 |
| 0.8127 | 0.5 | 40 | 0.8213 | 0.2803 | 0.8409 | 0.8409 | nan | 0.8409 | nan | 0.0 | 0.8409 | 0.0 |
| 0.5588 | 0.75 | 60 | 0.7310 | 0.4304 | 0.8608 | 0.8608 | nan | 0.8608 | nan | 0.0 | 0.8608 | nan |
| 0.6156 | 1.0 | 80 | 0.5317 | 0.3130 | 0.6261 | 0.6261 | nan | 0.6261 | nan | 0.0 | 0.6261 | nan |
| 0.5077 | 1.25 | 100 | 0.4617 | 0.3482 | 0.6964 | 0.6964 | nan | 0.6964 | nan | 0.0 | 0.6964 | nan |
| 0.5612 | 1.5 | 120 | 0.4336 | 0.3341 | 0.6683 | 0.6683 | nan | 0.6683 | nan | 0.0 | 0.6683 | nan |
| 0.4468 | 1.75 | 140 | 0.3946 | 0.3442 | 0.6883 | 0.6883 | nan | 0.6883 | nan | 0.0 | 0.6883 | nan |
| 0.292 | 2.0 | 160 | 0.3554 | 0.3041 | 0.6081 | 0.6081 | nan | 0.6081 | nan | 0.0 | 0.6081 | nan |
| 0.3769 | 2.25 | 180 | 0.3798 | 0.3402 | 0.6805 | 0.6805 | nan | 0.6805 | nan | 0.0 | 0.6805 | nan |
| 0.3386 | 2.5 | 200 | 0.3493 | 0.3147 | 0.6293 | 0.6293 | nan | 0.6293 | nan | 0.0 | 0.6293 | nan |
| 0.2689 | 2.75 | 220 | 0.3736 | 0.3492 | 0.6984 | 0.6984 | nan | 0.6984 | nan | 0.0 | 0.6984 | nan |
| 0.2539 | 3.0 | 240 | 0.3457 | 0.3368 | 0.6736 | 0.6736 | nan | 0.6736 | nan | 0.0 | 0.6736 | nan |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
|