Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10") 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("djbp/swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10") model = AutoModelForImageClassification.from_pretrained("djbp/swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7f264c4827c3f84b25d2c4697e93cdcda8a1132d97308258925a76c498f3f9f
- Size of remote file:
- 4.86 kB
- SHA256:
- e728bcdded801dbd0da58facc673ac292b219838937e7ba1594334bcbce851bd
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