---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_v2_r18vd
tags:
- generated_from_trainer
model-index:
- name: kvasir_seg_rtdetrv2_r18_test_fps
  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. -->

# kvasir_seg_rtdetrv2_r18_test_fps

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: 5.7736
- Map: 0.6786
- Map 50: 0.865
- Map 75: 0.7285
- Map Small: 0.0
- Map Medium: 0.5729
- Map Large: 0.6933
- Mar 1: 0.6607
- Mar 10: 0.8706
- Mar 100: 0.9114
- Mar Small: 0.0
- Mar Medium: 0.79
- Mar Large: 0.922
- Map Polyp: 0.6786
- Mar 100 Polyp: 0.9114

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map    | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1  | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Polyp | Mar 100 Polyp |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:---------:|:-------------:|
| 222.0855      | 1.0   | 100  | 27.8979         | 0.2324 | 0.3276 | 0.2381 | 0.0       | 0.0033     | 0.2491    | 0.4327 | 0.6583 | 0.7569  | 0.0       | 0.47       | 0.775     | 0.2324    | 0.7569        |
| 21.4558       | 2.0   | 200  | 8.5908          | 0.4453 | 0.6029 | 0.4705 | 0.0       | 0.2753     | 0.4596    | 0.6161 | 0.7815 | 0.872   | 0.0       | 0.71       | 0.8845    | 0.4453    | 0.872         |
| 11.9198       | 3.0   | 300  | 7.3897          | 0.559  | 0.7445 | 0.5928 | 0.0       | 0.3981     | 0.5748    | 0.6156 | 0.8218 | 0.8806  | 0.0       | 0.74       | 0.892     | 0.559     | 0.8806        |
| 10.1107       | 4.0   | 400  | 6.7691          | 0.5087 | 0.691  | 0.5261 | 0.0       | 0.258      | 0.53      | 0.6185 | 0.8047 | 0.8744  | 0.0       | 0.61       | 0.892     | 0.5087    | 0.8744        |
| 8.9974        | 5.0   | 500  | 6.1887          | 0.6247 | 0.7927 | 0.6818 | 0.0       | 0.51       | 0.6464    | 0.6682 | 0.8346 | 0.8872  | 0.0       | 0.69       | 0.9015    | 0.6247    | 0.8872        |
| 8.4837        | 6.0   | 600  | 6.0117          | 0.6313 | 0.82   | 0.6738 | 0.0       | 0.4952     | 0.6465    | 0.627  | 0.8502 | 0.9028  | 0.0       | 0.74       | 0.9155    | 0.6313    | 0.9028        |
| 7.9083        | 7.0   | 700  | 6.0504          | 0.6328 | 0.8234 | 0.6877 | 0.0       | 0.5697     | 0.6459    | 0.6412 | 0.8545 | 0.891   | 0.0       | 0.77       | 0.9015    | 0.6328    | 0.891         |
| 7.4364        | 8.0   | 800  | 5.9616          | 0.6493 | 0.8454 | 0.6945 | 0.0       | 0.6155     | 0.6611    | 0.6445 | 0.8635 | 0.9033  | 0.0       | 0.78       | 0.914     | 0.6493    | 0.9033        |
| 7.2255        | 9.0   | 900  | 5.7790          | 0.6767 | 0.8628 | 0.7297 | 0.0       | 0.5687     | 0.6914    | 0.663  | 0.8739 | 0.9047  | 0.0       | 0.79       | 0.915     | 0.6767    | 0.9047        |
| 6.9854        | 10.0  | 1000 | 5.7736          | 0.6786 | 0.865  | 0.7285 | 0.0       | 0.5729     | 0.6933    | 0.6607 | 0.8706 | 0.9114  | 0.0       | 0.79       | 0.922     | 0.6786    | 0.9114        |


### Framework versions

- Transformers 4.53.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1