vision
blackjack-basic-strategy
vision | blackjack-basic-strategy | |
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19 | 23 | |
15,475 | 26 | |
1.2% | - | |
9.4 | 2.0 | |
5 days ago | about 1 year ago | |
Python | JavaScript | |
BSD 3-clause "New" or "Revised" License | MIT License |
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vision
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Transitioning From PyTorch to Burn
Let's start by defining the ResNet module according to the Residual Network architecture, as replicated[1] by the torchvision implementation of the model we will import. Detailed architecture variants with a depth of 18, 34, 50, 101 and 152 layers can be found in the table below.
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Validation loss goes up after third epoch
The goal is to do keypoint-detection of fish (eg nose, tail etc) in a fishtank. By using a stereocamera for this, I'm also getting depth information which lets me measure the fish-length underwater. Im only training on RGB-Images though. I'm transfer-learning pytorch's keypoint-rcnn-resnet50, because thats the only available one in https://github.com/pytorch/vision/blob/main/torchvision/models/detection/keypoint_rcnn.py.
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Reading a DL paper: YOLO summary and discussion
Found relevant code at https://github.com/pytorch/vision + all code implementations here
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Open discussion and useful links people trying to do Object Detection
* Why doesnt Pytorch have YOLO! https://github.com/pytorch/vision/issues/6341
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My Neural Net is stuck, I've run out of ideas
Sorry to be annoying but I thought it was nice to give you some news as well. I was confused as to why there isnt yolo in pytorch, here it is why https://github.com/pytorch/vision/issues/6341
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Anyone ever get a virus from custom models?
The problem is the industry; People are still using .ckpt/.pth files to share weights, and unfortunately in their research work, they would need to reproduce the works of others. even pytorch include pretrained weights using pickles. https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py
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[Discussion] Stochastic Depth with BatchNorm ?
My question is more related to the variance of the batchs. If one batch contains samples that skip a connection and samples that do not ('row' mode in the Torchvision implementation), even if the values are ajusted to preserve the expected value, the variance will be much higher because we have in practice two distributions (for x_n and x_n + f(x_n)/p), which will mess up with the update of the batch normalization. Also, at inference time, all forward passes will be done as x_{n+1} = x_n + f(x_n), which has a different variance. The torchvision implementation also offers a 'batch' mode that kinda reduce this issue (because the global variance computed this way will be the mean of both distribution variances, instead of the variance of the joint distribution) but it does not seem to be the default mode (it does not even exist in the timm implementation).
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Solution for "RuntimeError: Couldn't load custom C++ ops"
RuntimeError: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.version and your torchvision version with torchvision.version and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.
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[D] My experience with running PyTorch on the M1 GPU
$ python vgg16-cifar10.py --device "cuda" torch 1.11.0+cu102 device cuda Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz 170499072it [00:46, 3628424.66it/s] Extracting data/cifar-10-python.tar.gz to data Downloading: "https://github.com/pytorch/vision/archive/v0.11.0.zip" to /home/md/.cache/torch/hub/v0.11.0.zip Epoch: 001/001 | Batch 0000/1406 | Loss: 2.6563 Epoch: 001/001 | Batch 0100/1406 | Loss: 2.4686 Epoch: 001/001 | Batch 0200/1406 | Loss: 2.1224 Epoch: 001/001 | Batch 0300/1406 | Loss: 2.1879 Epoch: 001/001 | Batch 0400/1406 | Loss: 2.1733 Epoch: 001/001 | Batch 0500/1406 | Loss: 2.2413 Epoch: 001/001 | Batch 0600/1406 | Loss: 2.0518 Epoch: 001/001 | Batch 0700/1406 | Loss: 2.1621 Epoch: 001/001 | Batch 0800/1406 | Loss: 1.9033 Epoch: 001/001 | Batch 0900/1406 | Loss: 1.8379 Epoch: 001/001 | Batch 1000/1406 | Loss: 1.9572 Epoch: 001/001 | Batch 1100/1406 | Loss: 1.8823 Epoch: 001/001 | Batch 1200/1406 | Loss: 1.7978 Epoch: 001/001 | Batch 1300/1406 | Loss: 2.0239 Epoch: 001/001 | Batch 1400/1406 | Loss: 1.8389 Time / epoch without evaluation: 6.75 min <------------------ Epoch: 001/001 | Train: 25.52% | Validation: 26.40% | Best Validation (Ep. 001): 26.40% Time elapsed: 9.03 min Total Training Time: 9.03 min Test accuracy 26.54% Total Time: 9.48 min
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Pytorch libraries
It is here in the source repository https://github.com/pytorch/vision/blob/main/torchvision/datasets/utils.py
blackjack-basic-strategy
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Show HN: Pip install inference, open source computer vision deployment
It’s an easy to use inference server for computer vision models.
The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).
It’s backed by a bunch of component pieces:
* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)
* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)
* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks
* foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models
* reusable utils to make adding support for new models easier
* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)
* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)
* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1])
Additionally, since it's focused specifically on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small-fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).
[1] https://universe.roboflow.com
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Open discussion and useful links people trying to do Object Detection
* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects.
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TensorFlow Datasets (TFDS): a collection of ready-to-use datasets
For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com
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Please suggest resources to learn how to work with pre-trained CV models
Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com
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Suggestion for identification problem with shipping labels?
If you're lacking training images, you can also use [Roboflow Universe](https://universe.roboflow.com) to obtain them (over 100 million labeled images available)
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Ask HN: Who is hiring? (November 2022)
Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 100k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. And we now host the largest collection[2] of open source computer vision datasets and pre-trained models[3].
We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. (We especially love hiring past and future founders.)
We're hiring 3 full-stack engineers this quarter and we're also looking for an infrastructure engineer with Elasticsearch experience.
[1]: https://docs.roboflow.com
[2]: https://blog.roboflow.com/computer-vision-datasets-and-apis/
[3]: https://universe.roboflow.com
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When annotating an image, if a collection of an entity changes the nature of the entity, do you label them collectively or separately?
Based on what I do/use when I prepare models: A good framework for creating and improving this dataset faster is to use Roboflow Universe and search “flowers” and “bouquets of flowers” in the search bar (it’s like Google Images for CV Datasets). You can search images by subject, or metadata, and clone them directly into a free public workspace (they house up to 10k images without charge). * https://universe.roboflow.com/ * https://universe.roboflow.com/search?q=flowers * https://universe.roboflow.com/search?q=bouqets
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
Lots of ideas will come to mind if you look and search through open source datasets: https://universe.roboflow.com/
- Ask HN: Any good self-hosted image recognition software?
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SAAS for object detection?
Open source datasets: https://universe.roboflow.com/ Model training: https://docs.roboflow.com/train Model deployment: https://docs.roboflow.com/inference/hosted-api
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
uxp-photoshop-plugin-samples - UXP Plugin samples for Photoshop 22 and higher.
torch2trt - An easy to use PyTorch to TensorRT converter
wallet - The official repository for the Valora mobile cryptocurrency wallet.
apple_m1_pro_python - A collection of ML scripts to test the M1 Pro MacBook Pro
process-google-dataset - Process Google Dataset is a tool to download and process images for neural networks from a Google Image Search using a Chrome extension and a simple Python code.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
rollup-react-example - An example React application using Rollup with ES modules, dynamic imports, Service Workers, and Flow.
functorch - functorch is JAX-like composable function transforms for PyTorch.
edenai-javascript - The best AI engines in one API: vision, text, speech, translation, OCR, machine learning, etc. SDK and examples for JavaScript developers.
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Speed-Coding-Games-in-JavaScript - Games Repository from Speed Coding channel