For any additional details, please check this PyTorch example here using ImageNet:
//github.com/pytorch/examples/tree/main/imagenet Answer from J_Johnson on discuss.pytorch.org
GitHub
github.com › pytorch › examples › tree › main › imagenet
examples/imagenet at main · pytorch/examples
If running on CUDA, you should always use the NCCL backend for multi-processing distributed training since it currently provides the best distributed training performance. For XPU multiprocessing is not supported as of PyTorch 2.6. python main.py -a resnet50 --dist-url 'tcp://127.0.0.1:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 [imagenet-folder with train and val folders]
Author pytorch
Medium
medium.com › we-talk-data › expert-guide-to-training-models-with-pytorchs-imagenet-dataset-927b69f80a76
Expert Guide to Training Models with PyTorch’s ImageNet Dataset | by Hey Amit | We Talk Data | Medium
April 18, 2025 - TPU (optional): If available, TPUs can also accelerate training for image classification tasks. Storage: Minimum of 500GB SSD, as the full ImageNet dataset can take up around 150GB, with more needed for model checkpoints and logs. To check your GPU availability in PyTorch, run this code:
NVIDIA
docs.nvidia.com › deeplearning › dali › user-guide › docs › examples › use_cases › pytorch › resnet50 › pytorch-resnet50.html
ImageNet Training in PyTorch — NVIDIA DALI
It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here. ... ln -s /path/to/train/jpeg/ train ln -s /path/to/validation/jpeg/ val torchrun --nproc_per_node=NUM_GPUS main.py -a resnet50 --dali_cpu --b 128 \ --loss-scale 128.0 --workers 4 --lr=0.4 --fp16-mode ./
PyTorch Forums
discuss.pytorch.org › t › guidance-on-training-model-with-pytorchs-imagenet-dataset › 196604
Guidance on Training Model with PyTorch's ImageNet Dataset - PyTorch Forums
February 4, 2024 - Hello PyTorch community, I’m seeking guidance on utilizing PyTorch’s torchvision.datasets.ImageNet class for training my model. Specifically, I’m interested in understanding how to effectively leverage the functionaliti…
Igor Moiseev
moiseevigor.github.io › software › 2022 › 12 › 18 › one-pager-training-resnet-on-imagenet
Train Resnet50 on ImageNet with PyTorch · Igor Moiseev
December 18, 2022 - A complete single-file training script for ResNet-50 on ImageNet using PyTorch — covering data loading, mixed-precision training, learning-rate scheduling, and top-1/top-5 accuracy tracking.
GitHub
github.com › tstandley › imagenet_training
GitHub - tstandley/imagenet_training: Pytorch code for training imagenet with fp16
Pytorch code for training imagenet with fp16. Contribute to tstandley/imagenet_training development by creating an account on GitHub.
Author tstandley
NVIDIA
docs.nvidia.com › deeplearning › dali › archives › dali_06_beta › dali-developer-guide › docs › examples › pytorch › renet50 › pytorch-resnet50.html
ImageNet training in PyTorch — NVIDIA DALI 0.6.0 documentation
It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here. ... ln -s /path/to/train/jpeg/ train ln -s /path/to/validation/jpeg/ val python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main.py -a resnet50 --dali_cpu --fp16 --b 128 --static-loss-scale 128.0 --workers 4 --lr=0.4 ./
PyTorch
docs.pytorch.org › vision › main › generated › torchvision.datasets.ImageNet.html
ImageNet — Torchvision main documentation
class torchvision.datasets.ImageNet(root: Union[str, Path], split: str = 'train', **kwargs: Any)[source]¶
NVIDIA
docs.nvidia.com › deeplearning › dali › archives › dali_0220_beta › user-guide › docs › examples › use_cases › pytorch › resnet50 › pytorch-resnet50.html
ImageNet training in PyTorch — NVIDIA DALI 0.22.0 documentation
It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here. ... ln -s /path/to/train/jpeg/ train ln -s /path/to/validation/jpeg/ val python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main.py -a resnet50 --dali_cpu --fp16 --b 128 --static-loss-scale 128.0 --workers 4 --lr=0.4 ./
Top answer 1 of 2
1
These are the detailed steps on how I obtained ImageNet and ran a PyTorch example training on it:
1. Go to https://www.image-net.org/download.php
2. Request to download ImageNet
3. Wait about 5 days for approval, write to them if the waiting period is over.
4. [I think you can skip this step] Download the Development Kit from the ILSVRC2017 page
5. Download the images from the ILSVRC2012 page
a. Training images (Task 1 & 2) 138 GB
b. Validation images (all tasks) 6.3 GB
c. Test images (all tasks) 13 GB
6. [I think you can skip this step if you use the script from step 8!] Unpack the tar files
a. mkdir val
b. tar -C val/ -xvf ILSVRC2012_img_val*.tar
c. mkdir test
d. tar -C test/ -xvf ILSVRC2012_img_test_v10102019.tar
e. mkdir train
f. tar -C train/ -xvf ILSVRC2012_img_train.tar
7. Confirm the number of images in each folder
a. ls val/ | wc -l # should give 50,000
b. ls test/ | wc -l # should give 100,000
8. Run the script extract_ILSVRC.sh from the PyTorch GitHub [https://github.com/pytorch/examples/blob/main/imagenet/extract_ILSVRC.sh]
# imagenet/train/
# ├── n01440764
# │ ├── n01440764_10026.JPEG
# │ ├── n01440764_10027.JPEG
# │ ├── ......
# ├── ......
# imagenet/val/
# ├── n01440764
# │ ├── ILSVRC2012_val_00000293.JPEG
# │ ├── ILSVRC2012_val_00002138.JPEG
# │ ├── ......
# ├── ......
9. Run a PyTorch example training on your ImageNet dataset [e.g. from the PyTorch examples GitHub repository https://github.com/pytorch/examples/blob/main/imagenet/main.py]
2 of 2
-1
ImageNet is available in torchvision datasets. https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageNet.html
PyTorch
docs.pytorch.org › vision › stable › generated › torchvision.datasets.ImageNet.html
ImageNet — Torchvision 0.27 documentation
class torchvision.datasets.ImageNet(root: Union[str, Path], split: str = 'train', **kwargs: Any)[source]¶
GitHub
github.com › pytorch › examples › blob › main › imagenet › main.py
examples/imagenet/main.py at main · pytorch/examples
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet', help='path to dataset (default: imagenet)') parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)') parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading worke
Author pytorch
Kaggle
kaggle.com › code › maunish › training-vae-on-imagenet-pytorch
Training VAE on ImageNet [Pytorch]
Checking your browser before accessing www.kaggle.com · Click here if you are not automatically redirected after 5 seconds
PyTorch Forums
discuss.pytorch.org › vision
How to speed up training on ImageNet - vision - PyTorch Forums
July 13, 2021 - To do so, I am using this examples/imagenet at master · pytorch/examples · GitHub as a framework. When I train it on the ImageNet, it takes around 16 hours per epoch on an A100, which is rather slow.
GitHub
github.com › floydhub › imagenet
GitHub - floydhub/imagenet: Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning) · GitHub
A full training on Imagenet can takes weeks according to the selected model. It's time to evaluate our model with some images(put the images you want to classify in the test/images folder): floyd run --gpu --env pytorch-0.2 --data ...
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NVIDIA
docs.nvidia.com › deeplearning › dali › archives › dali_0240 › user-guide › docs › examples › use_cases › pytorch › resnet50 › pytorch-resnet50.html
ImageNet training in PyTorch — NVIDIA DALI 0.24.0 documentation
It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here. ... ln -s /path/to/train/jpeg/ train ln -s /path/to/validation/jpeg/ val python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main.py -a resnet50 --dali_cpu --fp16 --b 128 --static-loss-scale 128.0 --workers 4 --lr=0.4 ./
Towards Data Science
towardsdatascience.com › home › latest › downloading and using the imagenet dataset with pytorch
Downloading and Using the ImageNet Dataset with PyTorch | Towards Data Science
January 21, 2025 - For example, it contains classes ... can be used for classification and object detection tasks and provides train, validation, and test splits by default....
GitHub
github.com › MadryLab › pytorch-example-imagenet
GitHub - MadryLab/pytorch-example-imagenet
This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset. Install PyTorch (pytorch.org) pip install -r requirements.txt · Download the ImageNet dataset from http://www.image-net.org/ ...
Author MadryLab
Kaggle
kaggle.com › code › yomnamabdulwahab › imagenet-pytorch
ImageNet Pytorch
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Medium
medium.com › @lokeshpara17 › tiny-imagenet-using-pytorch-42a3f2ee3c9d
Tiny ImageNet using PyTorch. I joined in FreeCodeCamp course where… | by Lokesh Para | Medium
August 17, 2020 - The course is named as “Deep Learning with PyTorch: Zero to GANs”. This is a 6 weeks course where we were trained from basic fundamentals, Feed forward neural networks, CNNs, transfer learning models, GANs etc., As part of the final course ...