Computer Science and Programming

NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT. source code:https://github.com/NVIDIA-AI-IOT/nanosam Note: source code includes 👍 Usage - ⏱ Performance - 🛠 Setup - 🤸 Examples - 🏋️ Training - 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also

NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT. source code:https://github.com/NVIDIA-AI-IOT/nanosam Note: source code includes 👍 Usage - ⏱ Performance - 🛠 Setup - 🤸 Examples - 🏋️ Training - 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also join us: @MachineLearning_Programming

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Stanford University has just opened full access to CS224U. One of their immensely popular graduate-level Natural Language Understanding course taught by Professor Christopher Potts. GitHub: https://github.com/cgpotts/cs224u YouTube: https://youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp @deeplearning_ai

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Segment Anything in High Quality We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Refer to our paper for more details. Our code and models will be released in two weeks. Stay tuned! https://github.com/syscv/sam-hq @deeplearning_ai

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ImageBind: One Embedding Space To Bind Them All. PyTorch implementation and pretrained models for ImageBind. For details, see the paper: ImageBind: One Embedding Space To Bind Them All. ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. Source code: https://github.com/facebookresearch/imagebind Paper: https://arxiv.org/pdf/2305.05665v1.pdf @deeplearning_ai

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