Instructions to use b07611031/cvt-w24-384-22k-finetuned-LeafType-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use b07611031/cvt-w24-384-22k-finetuned-LeafType-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="b07611031/cvt-w24-384-22k-finetuned-LeafType-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("b07611031/cvt-w24-384-22k-finetuned-LeafType-v1") model = AutoModelForImageClassification.from_pretrained("b07611031/cvt-w24-384-22k-finetuned-LeafType-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
cvt-w24-384-22k-finetuned-LeafType-v1
This model is a fine-tuned version of microsoft/cvt-w24-384-22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5823
- Accuracy: 0.8867
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Framework versions
- Transformers 4.38.1
- Pytorch 1.10.0+cu111
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for b07611031/cvt-w24-384-22k-finetuned-LeafType-v1
Base model
microsoft/cvt-w24-384-22kEvaluation results
- Accuracy on imagefoldervalidation set self-reported0.887