Instructions to use mouadenna/Mask2former-base-finetuned-segments-pv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mouadenna/Mask2former-base-finetuned-segments-pv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mouadenna/Mask2former-base-finetuned-segments-pv")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("mouadenna/Mask2former-base-finetuned-segments-pv") model = Mask2FormerForUniversalSegmentation.from_pretrained("mouadenna/Mask2former-base-finetuned-segments-pv") - Notebooks
- Google Colab
- Kaggle
Mask2former-base-finetuned-segments-pv
This model is a fine-tuned version of facebook/mask2former-swin-base-IN21k-ade-semantic on the mouadenna/satellite_PV_dataset_train_test dataset.
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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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