Instructions to use aspends/coco_binary_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aspends/coco_binary_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aspends/coco_binary_classification") 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("aspends/coco_binary_classification") model = AutoModelForImageClassification.from_pretrained("aspends/coco_binary_classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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tags:
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- generated_from_keras_callback
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- name: aspends/
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results: []
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# aspends/assignment
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0361
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- Validation Loss: 0.1211
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tags:
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- generated_from_keras_callback
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model-index:
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- name: aspends/coco_binary_classification
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results: []
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# aspends/assignment
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the COCO dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0361
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- Validation Loss: 0.1211
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