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Pretrained models on CIFAR10/100 in PyTorch

License: BSD 3-Clause "New" or "Revised" License

Python 92.21% Jupyter Notebook 7.79%

cifar10 cifar100 classification deep-learning pretrained-models pytorch-cifar-models notebook pytorch

pytorch-cifar-models's Issues

Do you share your training code?

Zip file downloaded is not able to extract using all the available unzipping tools

url = (
"https://rutgers.box.com/shared/static/gkw08ecs797j2et1ksmbg1w5t3idf5r5.zip"
)

    # Streaming, so we can iterate over the response.
    r = requests.get(url, stream=True, verify=False)

state_dicts.zip file downloaded is unable to get extracted, is it because I used verify=False in request.get() command?

CIFAR-10 normalize params

Thanks for the pre-trained models!

I'm testing the pre-trained models on CIFAR-10 using torchvision.datasets.CIFAR10() and found that you are using different normalization params from pytorch.org:

From your repo: mean: [0.4914, 0.4822, 0.4465], std: [0.2023, 0.1994, 0.2010].
From pytorch.org: transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)).

I can reproduce the results using the same normalization params as you had. It took me some time to locate the issue so I assume clarifying the normalization params somewhere in the project README would help other people.

requirements.txt not found

pip install throws the following error. requirements.txt is missing.

FileNotFoundError: [Errno 2] No such file or directory: 'requirements.txt'

Model's accuracy is different with your report on README.md when I evaluate the models.

Hi, I tried to evaluate your models' accuracy in two ways. In your report, cifar100_MobileNetV2_x1_0 model accuracy is 74.20, but when I tried

evaluate by your start_on_colab,
and evaluate by my own code

import torch
from torchvision import datasets, transforms
# data will be downloaded in data_directory.
data_directory = './data'
batchsize=256
device = 'cuda'
normalize = transforms.Normalize(mean=[0.507, 0.4865, 0.4409],
                                 std=[0.2673, 0.2564, 0.2761])

train_dataset = datasets.CIFAR100(root=data_directory,
                                  train=True,
                                  transform=transforms.Compose([
                                                                  transforms.RandomCrop(32, padding=4),
                                                                  transforms.RandomHorizontalFlip(),
                                                                  transforms.ToTensor(),
                                                                  normalize]),
                                  download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batchsize,
                                           shuffle=True,
                                           pin_memory=True,
                                           num_workers=2)

test_dataset = datasets.CIFAR100(root=data_directory,
                                 train=False,
                                 transform=transforms.Compose([
                                                                  transforms.ToTensor(),
                                                                  normalize]),
                                 download=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batchsize,
                                          shuffle=False,
                                          pin_memory=True,
                                          num_workers=2)


def validate(model,loader):
    global device
    model.eval()
    correct = 0.
    total = 0.
    for images, labels in loader:
        images = images.to(device)
        labels = labels.to(device)

        with torch.no_grad():
            pred = model(images)

        pred = torch.max(pred.data, 1)[1]
        total += labels.size(0)
        correct += (pred == labels).sum().item()

    val_acc = (correct / total)*100
    model.train()
    return val_acc
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar100_mobilenetv2_x1_0", pretrained=True).to(device)
validate(model, test_loader)

and I got

74.30 (+0.1)
74.29 (+0.09).

Can you check your models?

How to split the training set and the validation set?

Thanks for your pretrained-models on CIFAR10, I want to do some test with pretrained model, howerer, the inference acc(99.624%) seems too high (using torchvision.datasets.CFAR10). I think your train/val split approach is different with torchvision, could you tell me how your data set is divided?

`mobilenetv2_x0_75` trained on `Cifar100 raised HTTPError

All models worked (both for Cifar10 and 100); except mobilenetv2_x0_75 trained with Cifar100.

Downloading: "https://github.com/chenyaofo/pytorch-cifar-models/archive/master.zip" to C:\Users\xxx/.cache\torch\hub\master.zip
Downloading: "https://github.com/chenyaofo/pytorch-cifar-models/releases/download/mobilenetv2/cifar100_mobilenetv2_x0_75-9ab3e178.pt" to C:\Users\xxx/.cache\torch\hub\checkpoints\cifar100_mobilenetv2_x0_75-9ab3e178.p

...
...
 File "C:\ProgramData\Anaconda3\envs\PyTorchEight\lib\urllib\request.py", line 649, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)

HTTPError: Not Found

I thought it is the .pt filename, but looks correct to me.

Update:
When I try to download the .pt model directly (by on the link to the model), it does not download and I got the message Failed-No file.
Direct download works for all the other models.

Training details (hyper-parameters) and augmentation.

Can you provide the train.py ?

Can not load model

There is an Error: http.client.RemoteDisconnected: Remote end closed connection without response
Code:
net = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar10_resnet20", pretrained=True)

Environment:
Pytorch 1.11

Error running on Colab

Hi, I ran

!python -m entry.run --conf conf/cifar10.conf -o output/cifar10/resnet20 -M model.name=cifar10_resnet20

on colab, and it gives me the error

Traceback (most recent call last):
  File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/content/image-classification-codebase/entry/run.py", line 6, in <module>
    main(get_args())
  File "/content/image-classification-codebase/codebase/main.py", line 234, in main
    main_worker(local_rank, ngpus_per_node, args, args.conf)
  File "/content/image-classification-codebase/codebase/main.py", line 203, in main_worker
    prepare_for_training(conf, args.output_dir, local_rank)
  File "/content/image-classification-codebase/codebase/main.py", line 142, in prepare_for_training
    basic_bs = optimizer_config.pop("basic_bs")
  File "/usr/local/lib/python3.7/dist-packages/pyhocon/config_tree.py", line 274, in pop
    value = self.get(key, UndefinedKey)
  File "/usr/local/lib/python3.7/dist-packages/pyhocon/config_tree.py", line 236, in get
    return self._get(ConfigTree.parse_key(key), 0, default)
  File "/usr/local/lib/python3.7/dist-packages/pyhocon/config_tree.py", line 177, in _get
    u"No configuration setting found for key {key}".format(key='.'.join(key_path[:key_index + 1])))
pyhocon.exceptions.ConfigMissingException: 'No configuration setting found for key basic_bs'

Does anyone know how to solve this?

How to cite you?

Could let me know How I can cite your work

Question about pretrained models

Hi, I was wondering how many epochs were the pre-trained models trained for? And what other hyperparameters (learning rate, optimizer, scheduler, etc.) were used? I'm particularly interested in ResNet-20.
Thanks!

cifar10的weight decay

您好，我注意到论文中在cifar10上训练的resnet56微调时使用的weight decay是0.05，请问这是不是笔误呢。

Data preprocessing

I cannot find in the training logs how the data is being preprocessed for training and testing datasets. Does this mean that normalization only has been applied for both?

Mean and stds used for normalization

I couldn't find in the code/documentation the values used for mean/std of the normalization done if I want to test on new images, were they the same ones used in the image-classification-codebase code? ie,

mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]

Side note, on the get_vit_val_transforms(mean, std, img_size) and def get_vit_train_transforms(mean, std, img_size) functions there seems to be a small bug, you end up not using the values of mean/std passed in to the function and instead using hard coded ones (unless this is intended behaviour, but the mean/std parameters threw me off)

accuracy score

I downloaded some of the networks and the accuracy is different from what you mentioned.
For example after downloading the RES-NET I checked the accuracy and it was only 0.8 (on train).
Could you please check if this is a problem happens only to me?
(I down loaded the model from README and then uploaded into python)

Incorrect value for pretrained in the readme

model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar_resnet20", pretrained=true)
should be
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar_resnet20", pretrained="cifar10")
or
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar_resnet20", pretrained="cifar100")

chenyaofo / pytorch-cifar-models Goto Github PK

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