Resnet
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A comprehensive collection of PyTorch image models, layers, utilities, and training scripts for computer vision research and applications.
A comprehensive collection of machine learning algorithms implemented exclusively in NumPy for educational purposes and prototyping.
Official repository for Big Transfer (BiT) models, providing pre-trained visual representations for efficient transfer learning across computer vision tasks.
High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.
Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.
A sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
A collection of pretrained deep learning models (StyleGAN2, GPT2, VGG, ResNet) for the Jax/Flax ecosystem.
A PyTorch implementation of the DeepDream algorithm for generating psychedelic, dream-like images from neural network activations.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A fungal image classification project using ResNet to identify mushroom species from citizen science and expert sources.
Deploy a neural network model that transfers artistic styles from one image to another using a ResNet-based architecture.
A pre-trained image classifier that recognizes 365 different scene and location types using a ResNet18 model fine-tuned on Places365.
Sample code demonstrating Core ML integration with ResNet50 and custom models converted via coremltools.
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