---
base_model: toobiza/MT-ancient-spaceship-83
tags:
- generated_from_trainer
model-index:
- name: MT-proud-rain-95
  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. -->

# MT-proud-rain-95

This model is a fine-tuned version of [toobiza/MT-ancient-spaceship-83](https://huggingface.co/toobiza/MT-ancient-spaceship-83) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1562
- Loss Ce: 0.0000
- Loss Bbox: 0.0213
- Cardinality Error: 1.0
- Giou: 97.5230

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Loss Ce | Loss Bbox | Cardinality Error | Giou    |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-----------------:|:-------:|
| 0.1665        | 0.48  | 200  | 0.2101          | 0.0000  | 0.0293    | 1.0               | 96.8141 |
| 0.1967        | 0.97  | 400  | 0.1844          | 0.0000  | 0.0255    | 1.0               | 97.1659 |
| 0.1624        | 1.45  | 600  | 0.1833          | 0.0000  | 0.0253    | 1.0               | 97.1706 |
| 0.1594        | 1.93  | 800  | 0.1720          | 0.0000  | 0.0237    | 1.0               | 97.3363 |
| 0.1598        | 2.42  | 1000 | 0.1729          | 0.0000  | 0.0238    | 1.0               | 97.3105 |
| 0.1941        | 2.9   | 1200 | 0.1494          | 0.0000  | 0.0203    | 1.0               | 97.6099 |
| 0.1223        | 3.38  | 1400 | 0.1525          | 0.0000  | 0.0209    | 1.0               | 97.6036 |
| 0.1514        | 3.86  | 1600 | 0.1512          | 0.0000  | 0.0207    | 1.0               | 97.6045 |
| 0.1585        | 4.35  | 1800 | 0.1569          | 0.0000  | 0.0215    | 1.0               | 97.5391 |
| 0.128         | 4.83  | 2000 | 0.1535          | 0.0000  | 0.0210    | 1.0               | 97.5658 |
| 0.1089        | 5.31  | 2200 | 0.1594          | 0.0000  | 0.0220    | 1.0               | 97.5180 |
| 0.1624        | 5.8   | 2400 | 0.1650          | 0.0000  | 0.0228    | 1.0               | 97.4441 |
| 0.1074        | 6.28  | 2600 | 0.1648          | 0.0000  | 0.0227    | 1.0               | 97.4209 |
| 0.1693        | 6.76  | 2800 | 0.1554          | 0.0000  | 0.0212    | 1.0               | 97.5341 |
| 0.1075        | 7.25  | 3000 | 0.1595          | 0.0000  | 0.0218    | 1.0               | 97.4777 |
| 0.1271        | 7.73  | 3200 | 0.1570          | 0.0000  | 0.0215    | 1.0               | 97.5156 |
| 0.1293        | 8.21  | 3400 | 0.1549          | 0.0000  | 0.0211    | 1.0               | 97.5331 |
| 0.1143        | 8.7   | 3600 | 0.1564          | 0.0000  | 0.0214    | 1.0               | 97.5335 |
| 0.0966        | 9.18  | 3800 | 0.1555          | 0.0000  | 0.0213    | 1.0               | 97.5400 |
| 0.104         | 9.66  | 4000 | 0.1562          | 0.0000  | 0.0213    | 1.0               | 97.5230 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3