Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use fathyshalab/invoicevsadvertisement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fathyshalab/invoicevsadvertisement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fathyshalab/invoicevsadvertisement") 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("fathyshalab/invoicevsadvertisement") model = AutoModelForImageClassification.from_pretrained("fathyshalab/invoicevsadvertisement") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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@@ -5,12 +5,13 @@ datasets:
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- rvl_cdip
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metrics:
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- accuracy
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model-index:
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- name: invoicevsadvertisement
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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: rvl_cdip
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type: rvl_cdip
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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: 0.9892257579553997
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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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- rvl_cdip
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metrics:
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- accuracy
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base_model: microsoft/dit-base-finetuned-rvlcdip
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model-index:
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- name: invoicevsadvertisement
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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: rvl_cdip
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type: rvl_cdip
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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: 0.9892257579553997
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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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