Instructions to use YIMMYCRUZ/vit-model-ojas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YIMMYCRUZ/vit-model-ojas with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="YIMMYCRUZ/vit-model-ojas")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("YIMMYCRUZ/vit-model-ojas") model = AutoModelForImageClassification.from_pretrained("YIMMYCRUZ/vit-model-ojas") - Notebooks
- Google Colab
- Kaggle
vit-model-ojas
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.0099
- Accuracy: 1.0
Model description
You can manage to segment the images of plant leaves to be able to know if they are healthy or withered.
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1457 | 3.85 | 500 | 0.0099 | 1.0 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
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