Image Classification
Transformers
English
heritage
temple
damage-assessment
mixture-of-experts
Mixture of Experts
resnet50
efficientnet-b4
vit-base-patch16-224
yolo
Instructions to use monarch8661/moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monarch8661/moe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="monarch8661/moe") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("monarch8661/moe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- best.pt +3 -0
- efficientnet_b4_best.pth +3 -0
- efficientnet_b4_latest.pth +3 -0
- epoch0.pt +3 -0
- gate_best.pth +3 -0
- last.pt +3 -0
- resnet50_best.pth +3 -0
- resnet50_latest.pth +3 -0
- vit_b16_best.pth +3 -0
- vit_b16_latest.pth +3 -0
- yolo_damage_best.pth +3 -0
- yolo_damage_latest.pth +3 -0
best.pt
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efficientnet_b4_best.pth
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efficientnet_b4_latest.pth
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epoch0.pt
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gate_best.pth
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last.pt
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resnet50_best.pth
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resnet50_latest.pth
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vit_b16_best.pth
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vit_b16_latest.pth
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yolo_damage_best.pth
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yolo_damage_latest.pth
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