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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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

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
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Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

👉 @deeplearning_ai

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@deeplearning_ai

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NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.

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👉 @deeplearning_ai

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