Instructions to use kadiliis/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kadiliis/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="kadiliis/trainer_output")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("kadiliis/trainer_output") model = AutoModelForObjectDetection.from_pretrained("kadiliis/trainer_output") - Notebooks
- Google Colab
- Kaggle
trainer_output
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.7151
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: Use 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: 200
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8679 | 0.8 | 500 | 2.8766 |
| 1.7576 | 1.6 | 1000 | 2.7342 |
| 1.5051 | 2.4 | 1500 | 2.6522 |
| 1.3134 | 3.2 | 2000 | 2.5991 |
| 1.2242 | 4.0 | 2500 | 2.4941 |
| 1.2853 | 4.8 | 3000 | 2.4591 |
| 1.2261 | 5.6 | 3500 | 2.4325 |
| 1.1959 | 6.4 | 4000 | 2.4407 |
| 1.1454 | 7.2 | 4500 | 2.4451 |
| 1.0773 | 8.0 | 5000 | 2.4349 |
| 1.1067 | 8.8 | 5500 | 2.4102 |
| 1.0585 | 9.6 | 6000 | 2.4111 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.8.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
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Model tree for kadiliis/trainer_output
Base model
facebook/detr-resnet-50