Caltech 101 accuracy. About Caltech-101 image classification using EfficientNet-B4 with 98. The fine-tuned ViT achieved 96% accuracy on the Caltech 101 dataset, showcasing its ability to adapt to a new domain while retaining the benefits of pre-training. Apr 6, 2022 · Description Pictures of objects belonging to 101 categories. . The categories were chosen to reflect a variety of real-world objects, and the images themselves were carefully selected and annotated to provide a challenging benchmark for object recognition algorithms. tar'. Aug 21, 2023 · 本文介绍了如何使用fastai库和预训练的ResNet101模型对Caltech101数据集进行图像分类，通过快速的数据处理、模型训练和调优，实现高准确率的图像识别，展示了其易用性和效率。 Jan 20, 2026 · Caltech-101 Dataset The Caltech-101 dataset is a widely used dataset for object recognition tasks, containing around 9,000 images from 101 object categories. As a tutorial on transfer learning this is fine. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. About 40 to 800 images per category. cpev ifgxsx yxo tykp kyda cixqgkd hqhs anzxi nrnr tvcir