Instructions to use IksdeD/detr-fashionpedia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IksdeD/detr-fashionpedia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="IksdeD/detr-fashionpedia")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("IksdeD/detr-fashionpedia") model = AutoModelForObjectDetection.from_pretrained("IksdeD/detr-fashionpedia") - Notebooks
- Google Colab
- Kaggle
detr-fashionpedia
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: 1.2314
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 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: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 375 | 1.9030 |
| 2.4946 | 2.0 | 750 | 1.6074 |
| 1.6315 | 3.0 | 1125 | 1.5371 |
| 1.4693 | 4.0 | 1500 | 1.3845 |
| 1.4693 | 5.0 | 1875 | 1.3667 |
| 1.3499 | 6.0 | 2250 | 1.2746 |
| 1.2559 | 7.0 | 2625 | 1.2303 |
| 1.2142 | 8.0 | 3000 | 1.2098 |
| 1.2142 | 9.0 | 3375 | 1.1919 |
| 1.1755 | 10.0 | 3750 | 1.1837 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for IksdeD/detr-fashionpedia
Base model
facebook/detr-resnet-50