Instructions to use smc/PANDA_ConvNeXT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/PANDA_ConvNeXT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/PANDA_ConvNeXT") 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("smc/PANDA_ConvNeXT") model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ConvNeXT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from smc/PANDA_ConvNeXT: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/smc/PANDA_ConvNeXT/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://smc/PANDA_ConvNeXT@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/smc/PANDA_ConvNeXT/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.39 GB
- Xet hash:
- 32e5463e7e22fa0765d738e5db49ede3e8e3c16380c5c3e64403a126e609345f
- Size of remote file:
- 1.39 GB
- SHA256:
- e4ad6b01adf343a2278590a0fc63daf546c4f07a71fae1e06ef5fd1651deed8c
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