|
--- |
|
library_name: transformers |
|
license: other |
|
base_model: nvidia/mit-b0 |
|
tags: |
|
- vision |
|
- image-segmentation |
|
- generated_from_trainer |
|
model-index: |
|
- name: custom-object-test3 |
|
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-test3 |
|
|
|
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sungile/custom-object-masking3 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6728 |
|
- Mean Iou: nan |
|
- Mean Accuracy: nan |
|
- Overall Accuracy: nan |
|
- Accuracy Unknown: nan |
|
- Accuracy Background: nan |
|
- Accuracy Object: nan |
|
- Iou Unknown: nan |
|
- Iou Background: nan |
|
- 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: 2 |
|
|
|
### 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:| |
|
| 1.4981 | 0.25 | 20 | 1.5526 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 1.2809 | 0.5 | 40 | 1.4961 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 1.025 | 0.75 | 60 | 1.3031 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 0.9376 | 1.0 | 80 | 0.9104 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 0.8413 | 1.25 | 100 | 0.8012 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 0.7648 | 1.5 | 120 | 0.7258 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 0.7118 | 1.75 | 140 | 0.7015 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
| 0.6699 | 2.0 | 160 | 0.6728 | nan | nan | nan | nan | nan | nan | nan | nan | nan | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
