uoft-cs/cifar100
Viewer • Updated • 60k • 31.7k • 63
How to use edadaltocg/vit_base_patch16_224_in21k_ft_cifar100 with timm:
import timm
model = timm.create_model("hf_hub:edadaltocg/vit_base_patch16_224_in21k_ft_cifar100", pretrained=True)This model is a small timm/vit_base_patch16_224.orig_in21k_ft_in1k trained on cifar100.
Use the code below to get started with the model.
import timm
import torch
from torch import nn
model = timm.create_model("timm/vit_base_patch16_224.orig_in21k_ft_in1k",
pretrained=False)
model.head = nn.Linear(model.head.in_features, 100)
model.load_state_dict(
torch.hub.load_state_dict_from_url(
"https://huggingface.co/edadaltocg/vit_base_patch16_224_in21k_ft_cifar100/resolve/main/pytorch_model.bin",
map_location="cpu",
file_name="vit_base_patch16_224_in21k_ft_cifar100.pth",
)
)
Training data is cifar100.
config: scripts/train_configs/ft_cifar100.json
model: vit_base_patch16_224_in21k_ft_cifar100
dataset: cifar100
batch_size: 64
epochs: 10
validation_frequency: 1
seed: 1
criterion: CrossEntropyLoss
criterion_kwargs: {}
optimizer: SGD
lr: 0.01
optimizer_kwargs: {'momentum': 0.9, 'weight_decay': 0.0}
scheduler: CosineAnnealingLR
scheduler_kwargs: {'T_max': 10}
debug: False
Testing data is cifar100.
This model card was created by Eduardo Dadalto.