vision
Datasets, Transforms and Models specific to Computer Vision (by pytorch)
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, ... 🧠 (by lab-ml)
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| vision | nn | |
|---|---|---|
| 19 | 26 | |
| 15,374 | 47,503 | |
| 1.5% | 6.6% | |
| 9.5 | 7.7 | |
| 4 days ago | 25 days ago | |
| Python | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vision
Posts with mentions or reviews of vision.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-14.
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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
nn
Posts with mentions or reviews of nn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-09.
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Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
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[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
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[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
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Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
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[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
What are some alternatives?
When comparing vision and nn you can also consider the following projects:
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
torch2trt - An easy to use PyTorch to TensorRT converter
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
apple_m1_pro_python - A collection of ML scripts to test the M1 Pro MacBook Pro
functorch - functorch is JAX-like composable function transforms for PyTorch.
ZoeDepth - Metric depth estimation from a single image
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
onnx-simplifier - Simplify your onnx model
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.