Instructions to use AmineAllo/margin-element-detector-fm-clean-oath-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/margin-element-detector-fm-clean-oath-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/margin-element-detector-fm-clean-oath-16")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/margin-element-detector-fm-clean-oath-16") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/margin-element-detector-fm-clean-oath-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
margin-element-detector-fm-clean-oath-16
This model is a fine-tuned version of toobiza/MT-ancient-spaceship-83 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.9676
- eval_loss_ce: 0.0858
- eval_loss_bbox: 0.0424
- eval_cardinality_error: 0.3683
- eval_giou: 66.5256
- eval_runtime: 47.1617
- eval_samples_per_second: 18.977
- eval_steps_per_second: 4.75
- epoch: 7.53
- step: 17000
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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AmineAllo/MT-ancient-spaceship-83