Instructions to use Leoinhouse/ImagineClassification-finetuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leoinhouse/ImagineClassification-finetuned-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Leoinhouse/ImagineClassification-finetuned-model") 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("Leoinhouse/ImagineClassification-finetuned-model") model = AutoModelForImageClassification.from_pretrained("Leoinhouse/ImagineClassification-finetuned-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -47,9 +47,9 @@ On the **400-image** balanced test set; **runtime** = mean seconds per image for
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| Model | Test accuracy | Runtime (s / image) |
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| `google/vit-base-patch16-224-in21k` | **
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| `facebook/deit-tiny-patch16-224` | 0.9950 | 0.
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| `google/mobilenet_v2_1.0_224` | 0.9375 | 0.
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**Selected model:** `google/vit-base-patch16-224-in21k` (accuracy **1.0000** on this test split).
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| Model | Test accuracy | Runtime (s / image) |
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| `google/vit-base-patch16-224-in21k` | **0.9975** | 0.000953 |
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| `facebook/deit-tiny-patch16-224` | 0.9950 | 0.000886 |
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| `google/mobilenet_v2_1.0_224` | 0.9375 | 0.001365 |
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**Selected model:** `google/vit-base-patch16-224-in21k` (accuracy **1.0000** on this test split).
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