Instructions to use tanganke/convnext-base-224_gtsrb_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_gtsrb_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_gtsrb_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_gtsrb_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_gtsrb_sgd_batch-size-64_lr-0.01_steps-4000") - Notebooks
- Google Colab
- Kaggle
| - path.log_dir="outputs/convnext-base-224/gtsrb/batch_size=64,lr=0.01" | |
| - seed=0 | |
| - method=classification/image_classification_finetune | |
| - method.max_epochs=-1 | |
| - method.max_steps=4000 | |
| - method.save_top_k=-1 | |
| - method.save_interval=1000 | |
| - method.save_on_train_epoch_end=false | |
| - method.optimizer.lr=0.01 | |
| - method.lr_scheduler=null | |
| - method.dataloader_kwargs.batch_size=64 | |
| - modelpool=ConvNextForImageClassification/convnext-base-224 | |
| - modelpool.models._pretrained_.dataset_name=gtsrb | |
| - +dataset/image_classification/train@modelpool.train_datasets=gtsrb | |
| - +dataset/image_classification/test@modelpool.val_datasets=gtsrb | |