Detic
CLIP
Detic | CLIP | |
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11 | 103 | |
1,769 | 22,209 | |
1.0% | 2.5% | |
1.9 | 1.2 | |
about 1 month ago | 21 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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Detic
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Autodistill: A new way to create CV models
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
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[P] Image search with localization and open-vocabulary reranking.
For localisation at search time I ended up using OWL-ViT. This worked really well. I did not try Detic or CLIPseg but would be interested to hear if anyone else has tried these?
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training object detector using classified images?
git clone https://github.com/facebookresearch/Detic cd Detic pip install -r requirements python demo.py --config-file configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml --input desk.jpg --output out.jpg --vocabulary lvis --opts MODEL.WEIGHTS models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth
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[P] Any object detection library
You might want to take a look at DETIC : https://github.com/facebookresearch/Detic (Open Vocabulary Object Detection, trained on thousands of classes)
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[P] Awesome Image Segmentation Project Based on Deep Learning (5.6k star)
Are there any open-label segmentation model included in this repo, like Detic or LSeg?
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[R] CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory + Code + Robot demo
We made this using pretty recent advances in web-data pretrained models like Detic and LSeg for detection, CLIP for visual queries, and Sentence BERT for semantic queries. Our "database" is really a neural field (Instant NGP) that maps from 3D coordinates to a high dimensional embedding vector in the same representation space as CLIP and SBERT.
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[P] Using OpenAI's CLIP repository as a support, I was able to create a software to detect anything in an image at its original resolution!
Is it similar to the open vocabulary detic?
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Researchers at Meta and the University of Texas at Austin Propose ‘Detic’: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision
Code for https://arxiv.org/abs/2201.02605 found: https://github.com/facebookresearch/Detic
- Detecting Twenty-thousand Classes using Image-level Supervision
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[R] Detecting Twenty-thousand Classes using Image-level Supervision
github: https://github.com/facebookresearch/Detic
CLIP
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How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
What are some alternatives?
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
open_clip - An open source implementation of CLIP.
FasterRCNN - Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
disco-diffusion
clipseg - This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation