Instructions to use Hayloo9838/uno-recognizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hayloo9838/uno-recognizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hayloo9838/uno-recognizer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Hayloo9838/uno-recognizer") model = AutoModel.from_pretrained("Hayloo9838/uno-recognizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -67,7 +67,9 @@ To use this model you will need the pytorch and transformers libraries:
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```bash
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pip install torch transformers torchvision
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```
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Then go to the files of this HF page and simply use model.py in your python scripts, and that is it.
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## Limitations
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```bash
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pip install torch transformers torchvision
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```
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Then go to the files of this HF page and simply use model.py in your python scripts, and that is it.
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The current model.py will get an image from the internet, perform the processing, then make a heatmap of the attention (what the model cares about) and return its guess and confidence.
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On that animated image, it gets a pretty underestimating confidence of 30%, but that is because the dataset is insanely limited and i had to resort to augmentation, which is a game changer.
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## Limitations
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