Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/NMM_Classification_base_V10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/NMM_Classification_base_V10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/NMM_Classification_base_V10") 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("djbp/NMM_Classification_base_V10") model = AutoModelForImageClassification.from_pretrained("djbp/NMM_Classification_base_V10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from djbp/NMM_Classification_base_V10: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/djbp/NMM_Classification_base_V10/resolve/main/model.safetensors
- Command line
-
hf download hf://djbp/NMM_Classification_base_V10/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/djbp/NMM_Classification_base_V10/resolve/main/model.safetensors
348 MB
- Xet hash:
- 2a4ce0b282a8a7d9a505f2589ff87e4fe586a29ac89e9d83d998e1df598b4352
- Size of remote file:
- 348 MB
- SHA256:
- eafd06b03c614140c037637294b578195a8d8b30420c47bb31b27ebf3827f8c2
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