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---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_v2_r50vd
tags:
- generated_from_trainer
model-index:
- name: learn_hf-rt-detrv2-finetuned-on-trashify-dataset-video
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. -->
# learn_hf-rt-detrv2-finetuned-on-trashify-dataset-video
This model is a fine-tuned version of [PekingU/rtdetr_v2_r50vd](https://huggingface.co/PekingU/rtdetr_v2_r50vd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.1612
- Map: 0.4478
- Map 50: 0.5864
- Map 75: 0.5096
- Map Small: 0.0
- Map Medium: 0.2891
- Map Large: 0.4581
- Mar 1: 0.5121
- Mar 10: 0.7092
- Mar 100: 0.7613
- Mar Small: 0.0
- Mar Medium: 0.5975
- Mar Large: 0.7815
- Map Bin: 0.746
- Mar Bin: 0.9187
- Map Hand: 0.5961
- Mar Hand: 0.8136
- Map Not Bin: 0.0561
- Mar Not Bin: 0.5727
- Map Not Hand: 0.0185
- Mar Not Hand: 0.6333
- Map Not Trash: 0.2151
- Mar Not Trash: 0.6222
- Map Trash: 0.6585
- Mar Trash: 0.8397
- Map Trash Arm: 0.8444
- Mar Trash Arm: 0.9286
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### 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 Bin | Mar Bin | Map Hand | Mar Hand | Map Not Bin | Mar Not Bin | Map Not Hand | Mar Not Hand | Map Not Trash | Mar Not Trash | Map Trash | Mar Trash | Map Trash Arm | Mar Trash Arm |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-------:|:-------:|:--------:|:--------:|:-----------:|:-----------:|:------------:|:------------:|:-------------:|:-------------:|:---------:|:---------:|:-------------:|:-------------:|
| 90.2652 | 1.0 | 50 | 22.7169 | 0.2343 | 0.342 | 0.2447 | 0.0 | 0.0133 | 0.2432 | 0.3225 | 0.502 | 0.5748 | 0.0 | 0.0977 | 0.628 | 0.5467 | 0.8326 | 0.4655 | 0.65 | 0.007 | 0.45 | -1.0 | -1.0 | 0.0117 | 0.3264 | 0.3706 | 0.623 | 0.0044 | 0.5667 |
| 26.5507 | 2.0 | 100 | 13.5049 | 0.4103 | 0.5773 | 0.4496 | 0.02 | 0.1152 | 0.4293 | 0.4729 | 0.6547 | 0.7171 | 0.1 | 0.3898 | 0.7515 | 0.6658 | 0.8965 | 0.5505 | 0.7667 | 0.008 | 0.5071 | -1.0 | -1.0 | 0.1584 | 0.5111 | 0.6138 | 0.7876 | 0.4657 | 0.8333 |
| 19.0339 | 3.0 | 150 | 11.7506 | 0.4485 | 0.6168 | 0.528 | 0.0714 | 0.2235 | 0.4651 | 0.5086 | 0.7004 | 0.7429 | 0.25 | 0.5403 | 0.7789 | 0.6937 | 0.8894 | 0.596 | 0.7931 | 0.0112 | 0.55 | -1.0 | -1.0 | 0.1558 | 0.5931 | 0.6348 | 0.7982 | 0.5993 | 0.8333 |
| 16.4727 | 4.0 | 200 | 11.0906 | 0.5126 | 0.6896 | 0.5739 | 0.0 | 0.2155 | 0.5353 | 0.5473 | 0.7081 | 0.7555 | 0.0 | 0.4216 | 0.798 | 0.7116 | 0.8851 | 0.6232 | 0.7922 | 0.0478 | 0.55 | -1.0 | -1.0 | 0.2404 | 0.5819 | 0.6315 | 0.7903 | 0.8211 | 0.9333 |
| 15.0827 | 5.0 | 250 | 10.7144 | 0.4955 | 0.6776 | 0.5712 | 0.125 | 0.225 | 0.518 | 0.5428 | 0.695 | 0.7541 | 0.25 | 0.4699 | 0.7957 | 0.7618 | 0.9113 | 0.5424 | 0.7735 | 0.0344 | 0.5357 | -1.0 | -1.0 | 0.248 | 0.5986 | 0.6267 | 0.8053 | 0.7599 | 0.9 |
| 13.9634 | 6.0 | 300 | 10.6771 | 0.5377 | 0.7197 | 0.5907 | 0.2 | 0.3086 | 0.567 | 0.571 | 0.737 | 0.7894 | 0.2 | 0.5352 | 0.8348 | 0.7489 | 0.9121 | 0.5976 | 0.7951 | 0.1615 | 0.7214 | -1.0 | -1.0 | 0.2387 | 0.6 | 0.6643 | 0.808 | 0.8148 | 0.9 |
| 13.0714 | 7.0 | 350 | 10.4076 | 0.5525 | 0.7296 | 0.6065 | 0.2 | 0.1863 | 0.5876 | 0.5696 | 0.7384 | 0.781 | 0.2 | 0.3295 | 0.8492 | 0.7707 | 0.9078 | 0.629 | 0.8127 | 0.1764 | 0.5929 | -1.0 | -1.0 | 0.2363 | 0.5889 | 0.657 | 0.8168 | 0.8456 | 0.9667 |
| 12.405 | 8.0 | 400 | 10.2652 | 0.5346 | 0.7063 | 0.6085 | 0.3 | 0.2124 | 0.5697 | 0.55 | 0.7165 | 0.7691 | 0.3 | 0.3716 | 0.8306 | 0.7666 | 0.9028 | 0.5832 | 0.8118 | 0.1874 | 0.5786 | -1.0 | -1.0 | 0.2255 | 0.6153 | 0.644 | 0.8062 | 0.8007 | 0.9 |
| 11.7512 | 9.0 | 450 | 10.0407 | 0.5506 | 0.7358 | 0.6293 | 0.3 | 0.2311 | 0.5877 | 0.5614 | 0.7456 | 0.7755 | 0.3 | 0.4398 | 0.8337 | 0.7602 | 0.9142 | 0.6356 | 0.8029 | 0.2287 | 0.5857 | -1.0 | -1.0 | 0.2414 | 0.6306 | 0.6674 | 0.8195 | 0.7705 | 0.9 |
| 11.3543 | 10.0 | 500 | 10.0798 | 0.5401 | 0.7284 | 0.6267 | 0.3 | 0.2092 | 0.5756 | 0.5473 | 0.7485 | 0.7801 | 0.3 | 0.3767 | 0.8405 | 0.7629 | 0.905 | 0.6323 | 0.8059 | 0.2095 | 0.5857 | -1.0 | -1.0 | 0.2192 | 0.6278 | 0.6597 | 0.823 | 0.7569 | 0.9333 |
### Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
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