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OrangeRecognizer
jarric/OrangeRecognizer
OrangeRecognizer is a machine learning model from jarric. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Dataset: https://www.kaggle.com/datasets/jarricgentletail/mobilenetv3-preprocessed-orange-disease-fruit-dset
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Updated Apr 21, 2025
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Apr 21, 2025
- Updated
- Apr 21, 2025
- SHA
- 51390844
Model card
From the Hugging Face model README
Dataset: https://www.kaggle.com/datasets/jarricgentletail/mobilenetv3-preprocessed-orange-disease-fruit-dset
Download model and to run use the following code:
torch.serialization.add_safe_globals([MobileNetV3])
torch.serialization.add_safe_globals([Sequential])
torch.serialization.add_safe_globals([Conv2dNormActivation])
torch.serialization.add_safe_globals([Conv2d])
torch.serialization.add_safe_globals([BatchNorm2d])
torch.serialization.add_safe_globals([Hardswish])
torch.serialization.add_safe_globals([InvertedResidual])
torch.serialization.add_safe_globals([ReLU])
torch.serialization.add_safe_globals([SqueezeExcitation])
torch.serialization.add_safe_globals([AdaptiveAvgPool2d])
torch.serialization.add_safe_globals([Hardsigmoid])
torch.serialization.add_safe_globals([Linear])
torch.serialization.add_safe_globals([Dropout])
mobilenetv3 = torch.load("<target_path>")
I kept saving whole class instead of just the state dict. The model was finetuned, based on IMAGENETV1 dataset. I just finetuned the classification head.
- License
- apache-2.0
- Languages
- en
- Created
- Apr 21, 2025
- Updated
- Apr 21, 2025