Coco Collection

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🔮Psychic

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“We write your reusable computer vision tools. 💜”

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🔮Psychic

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“Framework agnostic sliced/tiled inference + interactive ui + error analysis plots”

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🔮Psychic

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“Tensorflow Faster RCNN for Object Detection”

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🔮Psychic

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“Mask RCNN in TensorFlow ”

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📦Normal

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“:pencil2: Web-based image segmentation tool for object detection, localization, and keypoints”

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📦Normal

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“[CVPR 2023] OneFormer: One Transformer to Rule Universal Image Segmentation”

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🔮Psychic

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“A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.”

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🔮Psychic

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“Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch”

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🔮Psychic

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“Real-time and accurate open-vocabulary end-to-end object detection”

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🔮Psychic

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“Two-stage CenterNet”

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🔮Psychic

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“Convert COCO JSON to YOLO format”

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🔮Psychic

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“convert dataset to coco/voc format”

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🔮Psychic

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“PyTorch re-implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC datasets”

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🔥Flame

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“To speedup and simplify image labeling/ annotation process with multiple supported formats.”

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🔮Psychic

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“[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention”

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🔮Psychic

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“The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf”

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📦Normal

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“Object Detection”

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🔮Psychic

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“Helper functions to create COCO datasets”