Instructions to use tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000") 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("tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download program_report.json from tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000: direct link, hf CLI and curl.
- Browser
- Download file 99 Bytes
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https://huggingface.co/tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/resolve/main/program_report.json
- Command line
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hf download hf://tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/program_report.json
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curl -L -o program_report.json https://huggingface.co/tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/resolve/main/program_report.json
99 Bytes
| {"model_info": {"trainable_params": 87614639, "all_params": 87614639, "trainable_percentage": 1.0}} |