Instructions to use micole66/autotrain-pachyderms-v3-2088867198 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micole66/autotrain-pachyderms-v3-2088867198 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="micole66/autotrain-pachyderms-v3-2088867198") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("micole66/autotrain-pachyderms-v3-2088867198", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit From AutoTrain
Browse files- .gitattributes +3 -0
- README.md +32 -0
- config.json +3 -0
- preprocessor_config.json +3 -0
- pytorch_model.bin +3 -0
.gitattributes
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README.md
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---
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tags:
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- autotrain
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- vision
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- image-classification
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datasets:
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- micole66/autotrain-data-pachyderms-v3
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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example_title: Tiger
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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example_title: Teapot
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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example_title: Palace
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co2_eq_emissions:
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emissions: 1.5875017032028493
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---
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# Model Trained Using AutoTrain
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- Problem type: Binary Classification
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- Model ID: 2088867198
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- CO2 Emissions (in grams): 1.5875
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## Validation Metrics
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- Loss: 0.020
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- Accuracy: 1.000
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- Precision: 1.000
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- Recall: 1.000
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- AUC: 1.000
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- F1: 1.000
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config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:dc73acfe5a7867d1463a475030ebb1522e4ef2f795ebc451c47a49134f6676dd
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size 756
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preprocessor_config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:00ccdf78c8f0e355e18ea2a21f5502a82d8b7c263d0de03eab634c553b764092
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size 265
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 1392724369
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