---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_v2_r18vd
tags:
- generated_from_trainer
model-index:
- name: rt-detr-v2_barcode-detection
  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. -->

# rt-detr-v2_barcode-detection

This model is a fine-tuned version of [PekingU/rtdetr_v2_r18vd](https://huggingface.co/PekingU/rtdetr_v2_r18vd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0208
- Map: 0.6944
- Map 50: 0.8578
- Map 75: 0.7542
- Map Small: 0.2808
- Map Medium: 0.7135
- Map Large: 0.7651
- Mar 1: 0.3706
- Mar 10: 0.8307
- Mar 100: 0.8611
- Mar Small: 0.6131
- Mar Medium: 0.863
- Mar Large: 0.879
- Map Barcode: 0.6944
- Mar 100 Barcode: 0.8611

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map    | Map 50 | Map 75 | Map Barcode | Map Large | Map Medium | Map Small | Mar 1  | Mar 10 | Mar 100 | Mar 100 Barcode | Mar Large | Mar Medium | Mar Small |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-----------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------------:|:---------:|:----------:|:---------:|
| 7.43          | 1.0   | 1636 | 5.0547          | 0.623  | 0.7756 | 0.6841 | 0.623       | 0.6594    | 0.6674     | 0.2648    | 0.3542 | 0.8117 | 0.8521  | 0.8521          | 0.8749    | 0.8552     | 0.5325    |
| 6.9538        | 2.0   | 3272 | 4.9508          | 0.6297 | 0.7829 | 0.6878 | 0.261       | 0.6449    | 0.7109     | 0.3612    | 0.8238 | 0.8604 | 0.5972  | 0.8624          | 0.8794    | 0.6297     | 0.8604    |
| 6.6733        | 3.0   | 4908 | 5.0056          | 0.6553 | 0.8073 | 0.7122 | 0.2319      | 0.6782    | 0.7359     | 0.3481    | 0.8201 | 0.8542 | 0.6107  | 0.8573          | 0.8712    | 0.6553     | 0.8542    |
| 6.5783        | 4.0   | 6544 | 5.0493          | 0.6524 | 0.808  | 0.711  | 0.2391      | 0.6782    | 0.7472     | 0.3581    | 0.8268 | 0.8629 | 0.6066  | 0.8639          | 0.8819    | 0.6524     | 0.8629    |
| 6.4986        | 5.0   | 8180 | 5.0208          | 0.6944 | 0.8578 | 0.7542 | 0.2808      | 0.7135    | 0.7651     | 0.3706    | 0.8307 | 0.8611 | 0.6131  | 0.863           | 0.879     | 0.6944     | 0.8611    |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.4.2
- Tokenizers 0.22.1