Instructions to use ENOT-AutoDL/imagenet-benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENOT-AutoDL/imagenet-benchmark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ENOT-AutoDL/imagenet-benchmark") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ENOT-AutoDL/imagenet-benchmark", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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MobileNetV2/MobileNetV2-ENOT-x1_6.onnx
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MobileNetV2/MobileNetV2-ENOT-x1_6.pth
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README.md
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| Model | Latency (MMACs) | Accuracy (%) |
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| **MobileNetV2 Torchvision** | 334.23 | 71.88 |
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| **MobileNetV2 ENOT (x1.6)** | 209.
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| **MobileNetV2 ENOT (x2.1)** | 156.80 (x2.13) | 69.90 (-1.98) |
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# Validation
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| Model | Latency (MMACs) | Accuracy (%) |
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| **MobileNetV2 Torchvision** | 334.23 | 71.88 |
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| **MobileNetV2 ENOT (x1.6)** | 209.24 (x1.6) | 71.38 (-0.5) |
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| **MobileNetV2 ENOT (x2.1)** | 156.80 (x2.13) | 69.90 (-1.98) |
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# Validation
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