Instructions to use aspends/coco_multiclass_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aspends/coco_multiclass_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aspends/coco_multiclass_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("aspends/coco_multiclass_classification") model = AutoModelForImageClassification.from_pretrained("aspends/coco_multiclass_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from aspends/coco_multiclass_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/aspends/coco_multiclass_classification/resolve/main/tf_model.h5
- Command line
-
hf download hf://aspends/coco_multiclass_classification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/aspends/coco_multiclass_classification/resolve/main/tf_model.h5
343 MB
- Xet hash:
- 695926aaad6fb8f14ad84bf81b61dfff55c02617706f850a3e4cf8db4b615374
- Size of remote file:
- 343 MB
- SHA256:
- 7210bdb00e6054fcfc2b842e43fa3bc5ab2b9b306b3640354989cd808e7da413
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