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This is a series of computer vision foundational projects that anyone diving into the field must tackle.
Implementation of VisionLLaMA from the paper: "VisionLLaMA: A Unified LLaMA Interface for Vision Tasks" in PyTorch and Zeta
Implementation of Midas from [Towards Robust Monocular Depth Estimation] in Pytorch and Zeta
Enhance your skills in prompt engineering for vision models. Learn to effectively prompt, fine-tune, and track experiments for models like SAM, OWL-ViT, and Stable Diffusion 2.0 to achieve precise image generation, segmentation, and object detection.
In This repo i FineTuned a Pretrained ResNet18 model from PyTorch library
A framework to compute threshold sensitivity of deep networks to visual stimuli.
building AVA from ex-machina; a lightweight multi-modal system from scratch, just for learning & experimentation
Vision-based swarms in the Presence of Occlusions