Rt Detr v2 Finetuned
This model is a fine-tuned version of PekingU/rtdetrv2r50vd on an unknown dataset. It achieves the following results on the evaluation set: Loss: 9.7605 Map: 0.43 Map 50: 0.6 Map 75: 0.5 Map Small: 0.0 Map Medium:...
Modellquelle
- Art
- Other
- Dateigröße
- 164 MB
- Basismodell
- PekingU/rtdetr_v2_r50vd
- Version
- vRevision 5383a75
- Aktualisiert
- 16. Sept. 2026
- Downloads
- 3
- Quelle
- Hugging Face
Quellenbeschreibung
rt-detr-v2-finetuned
This model is a fine-tuned version of PekingU/rtdetr_v2_r50vd on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 9.7605
- Map: 0.43
- Map 50: 0.6
- Map 75: 0.5
- Map Small: 0.0
- Map Medium: 0.19
- Map Large: 0.45
- Mar 1: 0.6
- Mar 10: 0.71
- Mar 100: 0.73
- Mar Small: 0.0
- Mar Medium: 0.44
- Mar Large: 0.76
- Map Bin: 0.78
- Mar Bin: 0.88
- Map Hand: 0.55
- Mar Hand: 0.76
- Map Not Bin: 0.13
- Mar Not Bin: 0.67
- Map Not Hand: 0.01
- Mar Not Hand: 0.6
- Map Not Trash: 0.18
- Mar Not Trash: 0.54
- Map Trash: 0.61
- Mar Trash: 0.79
- Map Trash Arm: 0.76
- Mar Trash Arm: 0.88
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_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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 82.2808 | 1.0 | 50 | 19.0962 | 0.09 | 0.13 | 0.1 | 0.0 | 0.01 | 0.1 | 0.2 | 0.4 | 0.45 | 0.0 | 0.13 | 0.48 | 0.18 | 0.87 | 0.26 | 0.81 | 0.01 | 0.5 | 0.0 | 0.0 | 0.0 | 0.26 | 0.19 | 0.7 | 0.0 | 0.0 |
| 25.2194 | 2.0 | 100 | 11.3634 | 0.29 | 0.41 | 0.32 | 0.0 | 0.1 | 0.3 | 0.38 | 0.68 | 0.75 | 0.0 | 0.42 | 0.78 | 0.73 | 0.9 | 0.52 | 0.77 | 0.01 | 0.63 | 0.0 | 0.8 | 0.11 | 0.55 | 0.57 | 0.77 | 0.08 | 0.82 |
| 18.5745 | 3.0 | 150 | 10.0971 | 0.4 | 0.54 | 0.45 | 0.0 | 0.14 | 0.41 | 0.43 | 0.69 | 0.76 | 0.0 | 0.49 | 0.79 | 0.77 | 0.88 | 0.58 | 0.82 | 0.07 | 0.72 | 0.0 | 0.6 | 0.13 | 0.61 | 0.62 | 0.78 | 0.61 | 0.88 |
| 16.2489 | 4.0 | 200 | 9.8114 | 0.41 | 0.56 | 0.47 | 0.0 | 0.2 | 0.42 | 0.48 | 0.7 | 0.77 | 0.0 | 0.52 | 0.8 | 0.79 | 0.88 | 0.55 | 0.8 | 0.06 | 0.75 | 0.0 | 0.7 | 0.17 | 0.6 | 0.62 | 0.8 | 0.66 | 0.85 |
| 14.9636 | 5.0 | 250 | 9.6389 | 0.41 | 0.56 | 0.47 | 0.0 | 0.29 | 0.43 | 0.5 | 0.7 | 0.75 | 0.0 | 0.47 | 0.78 | 0.79 | 0.89 | 0.56 | 0.8 | 0.1 | 0.67 | 0.0 | 0.6 | 0.16 | 0.6 | 0.61 | 0.8 | 0.7 | 0.88 |
| 13.9524 | 6.0 | 300 | 9.5040 | 0.43 | 0.6 | 0.5 | 0.0 | 0.18 | 0.45 | 0.52 | 0.71 | 0.76 | 0.0 | 0.56 | 0.78 | 0.79 | 0.88 | 0.57 | 0.8 | 0.12 | 0.72 | 0.0 | 0.7 | 0.18 | 0.58 | 0.59 | 0.8 | 0.75 | 0.82 |
| 13.1142 | 7.0 | 350 | 9.6180 | 0.43 | 0.59 | 0.5 | 0.0 | 0.31 | 0.45 | 0.53 | 0.72 | 0.74 | 0.0 | 0.46 | 0.77 | 0.79 | 0.88 | 0.57 | 0.78 | 0.16 | 0.65 | 0.0 | 0.6 | 0.17 | 0.56 | 0.61 | 0.8 | 0.72 | 0.9 |
| 12.5272 | 8.0 | 400 | 9.6804 | 0.43 | 0.59 | 0.49 | 0.0 | 0.19 | 0.45 | 0.61 | 0.72 | 0.74 | 0.0 | 0.45 | 0.77 | 0.78 | 0.88 | 0.55 | 0.78 | 0.11 | 0.68 | 0.01 | 0.6 | 0.18 | 0.55 | 0.61 | 0.8 | 0.74 | 0.88 |
| 12.0791 | 9.0 | 450 | 9.7290 | 0.41 | 0.58 | 0.49 | 0.0 | 0.17 | 0.43 | 0.6 | 0.71 | 0.72 | 0.0 | 0.43 | 0.76 | 0.77 | 0.88 | 0.54 | 0.76 | 0.11 | 0.67 | 0.01 | 0.6 | 0.18 | 0.53 | 0.6 | 0.78 | 0.69 | 0.85 |
| 11.7099 | 10.0 | 500 | 9.7605 | 0.43 | 0.6 | 0.5 | 0.0 | 0.19 | 0.45 | 0.6 | 0.71 | 0.73 | 0.0 | 0.44 | 0.76 | 0.78 | 0.88 | 0.55 | 0.76 | 0.13 | 0.67 | 0.01 | 0.6 | 0.18 | 0.54 | 0.61 | 0.79 | 0.76 | 0.88 |
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 2. Sept.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r50vd tags: - generated_from_trainer model-index: - name: rt-detr-v2-finetuned 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-finetuned 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: 9.7605 - Map: 0.43 - Map 50: 0.6 - Map 75: 0.5 - Map Small: 0.0 - Map Medium: 0.19 - Map Large: 0.45 - Mar 1: 0.6 - Mar 10: 0.71 - Mar 100: 0.73 - Mar Small: 0.0 - Mar Medium: 0.44 - Mar Large: 0.76 - Map Bin: 0.78 - Mar Bin: 0.88 - Map Hand: 0.55 - Mar Hand: 0.76 - Map Not Bin: 0.13 - Mar Not Bin: 0.67 - Map Not Hand: 0.01 - Mar Not Hand: 0.6 - Map Not Trash: 0.18 - Mar Not Trash: 0.54 - Map Trash: 0.61 - Mar Trash: 0.79 - Map Trash Arm: 0.76 - Mar Trash Arm: 0.88 ## 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_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: 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 | |:-------------:|:-----:|:----:|:---------------:|:----:|:------:|:------:|:---------:|:----------:|:---------:|:-----:|:------:|:-------:|:---------:|:----------:|:---------:|:-------:|:-------:|:--------:|:--------:|:-----------:|:-----------:|:------------:|:------------:|:-------------:|:-------------:|:---------:|:---------:|:--...
Source context: 3 downloads · 0 likes · Pipeline object-detection · Library transformers · Repo ashutosh-kedar/rt-detr-v2-finetuned
