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SOTA Image Classification with PyTorch by Guy Anthony NAMA NYAM is a document available to read on EtoBox.
What is SOTA Image Classification with PyTorch about?
1. The author improved image classification accuracy on a Stanford dogs dataset from 91.35% to 94.73% using a pre-trained ResNeXt-101 model and FixRes method for fine-tuning. 2. They used a pre-trained ResNeXt-101 model as a fixed feature extractor and fine-tuned only the last linear layer on the Stanford dogs dataset. 3. The model was trained for 30 epochs with early stopping, achieving a best validation accuracy of 94.07% and reducing validation loss to a minimum of 0.3177.
- Author
- Guy Anthony NAMA NYAM
- Language
- EN