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")
https://huggingface.co/tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/resolve/main/hparams.yaml
hf download hf://tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/hparams.yaml
curl -L -o hparams.yaml https://huggingface.co/tanganke/convnext-base-224_dtd_sgd_batch-size-64_lr-0.01_steps-4000/resolve/main/hparams.yaml
