Instructions to use tanganke/convnext-base-224_mnist_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_mnist_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_mnist_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_mnist_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,291 Bytes
2bb4112 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | [2026-01-07 17:52:59,419][fusion_bench.programs.fusion_program][INFO] - Running the model fusion program.
[2026-01-07 17:52:59,422][fusion_bench.programs.fusion_program][INFO] - loading model pool
[2026-01-07 17:53:01,536][fusion_bench.programs.fusion_program][INFO] - loading method
[2026-01-07 17:53:01,591][fusion_bench.method.classification.image_classification_finetune][INFO] - Training interval: step
[2026-01-07 17:53:01,592][fusion_bench.method.classification.image_classification_finetune][INFO] - Max epochs: -1, max steps: 4000
[2026-01-07 17:53:01,593][fusion_bench.programs.fusion_program][INFO] - loading task pool
[2026-01-07 17:53:03,190][fusion_bench.method.classification.image_classification_finetune][INFO] - Number of classes for dataset mnist: 10
[2026-01-07 17:53:03,203][datasets.load][WARNING] - Using the latest cached version of the dataset since mnist couldn't be found on the Hugging Face Hub (offline mode is enabled).
[2026-01-07 17:53:03,211][datasets.packaged_modules.cache.cache][WARNING] - Found the latest cached dataset configuration 'mnist' at /data/dataset/datasets/mnist/mnist/0.0.0/77f3279092a1c1579b2250db8eafed0ad422088c (last modified on Mon Jan 5 04:21:12 2026).
[2026-01-07 17:53:03,516][fusion_bench.method.classification.image_classification_finetune][INFO] - Training dataset size: 60000
[2026-01-07 17:53:03,540][datasets.load][WARNING] - Using the latest cached version of the dataset since mnist couldn't be found on the Hugging Face Hub (offline mode is enabled).
[2026-01-07 17:53:03,547][datasets.packaged_modules.cache.cache][WARNING] - Found the latest cached dataset configuration 'mnist' at /data/dataset/datasets/mnist/mnist/0.0.0/77f3279092a1c1579b2250db8eafed0ad422088c (last modified on Mon Jan 5 04:21:12 2026).
[2026-01-07 17:53:03,615][fusion_bench.method.classification.image_classification_finetune][INFO] - optimizer:
SGD (
Parameter Group 0
dampening: 0
differentiable: False
foreach: None
fused: None
lr: 0.01
maximize: False
momentum: 0.9
nesterov: False
weight_decay: 0.0001
)
[2026-01-07 18:31:07,790][fusion_bench.method.classification.image_classification_finetune][INFO] - Saving the final model to outputs/convnext-base-224/mnist/batch_size=64,lr=0.01/raw_checkpoints/final
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