Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use poolrf2001/platzi-vit-model-pool-river with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poolrf2001/platzi-vit-model-pool-river with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="poolrf2001/platzi-vit-model-pool-river") 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("poolrf2001/platzi-vit-model-pool-river") model = AutoModelForImageClassification.from_pretrained("poolrf2001/platzi-vit-model-pool-river") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update Hugging Face dataset ID
#1
by librarian-bot - opened
README.md
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@@ -3,15 +3,15 @@ license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- beans
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metrics:
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- accuracy
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model-index:
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- name: platzi-vit-model-pool-river
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: beans
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type: beans
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split: train
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args: default
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metrics:
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type: accuracy
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value: 1.0
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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tags:
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- generated_from_trainer
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datasets:
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- AI-Lab-Makerere/beans
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metrics:
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- accuracy
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model-index:
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- name: platzi-vit-model-pool-river
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results:
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- task:
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type: image-classification
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name: Image Classification
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dataset:
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name: beans
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type: beans
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split: train
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args: default
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metrics:
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- type: accuracy
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value: 1.0
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name: Accuracy
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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