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# SPIE_MULTICLASS_NVIDIA_1_2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1870
- Accuracy: 0.9408

## 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: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6153        | 0.9886 | 65   | 0.4135          | 0.8475   |
| 0.3616        | 1.9924 | 131  | 0.2620          | 0.9008   |
| 0.3104        | 2.9962 | 197  | 0.2363          | 0.9233   |
| 0.2635        | 4.0    | 263  | 0.2104          | 0.9283   |
| 0.233         | 4.9430 | 325  | 0.1870          | 0.9408   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

burn through contamination good weld lack of fusion lack of penetration misalignment