Update timm.md

[kadirnar]

import timm
import torch
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

# Load from Hub 🔥
model = timm.create_model(
    'hf-hub:nateraw/resnet50-oxford-iiit-pet',
    pretrained=True
)

# Set model to eval mode for inference
model.eval()

# Create Transform
transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))
print(model.pretrained_cfg)

Output:

{'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth',
 'hf_hub_id': 'nateraw/resnet50-oxford-iiit-pet',
 'source': 'hf-hub',
 'architecture': 'resnet50',
 'custom_load': False,
 'input_size': [3, 224, 224],
 'fixed_input_size': False,
 'interpolation': 'bicubic',
 'crop_pct': 0.95,
 'crop_mode': 'center',
 'mean': [0.485, 0.456, 0.406],
 'std': [0.229, 0.224, 0.225],
 'num_classes': 37,
 'label_names': ['Abyssinian',
  'american_bulldog',
  'american_pit_bull_terrier',
  'basset_hound',
  'beagle',
  'Bengal',
  'Birman',
  'Bombay',
  'boxer',
  'British_Shorthair',
  'chihuahua',
  'Egyptian_Mau',
...
  'wheaten_terrier',
  'yorkshire_terrier'],
 'pool_size': [7, 7],
 'first_conv': 'conv1',
 'classifier': 'fc'}

[HuggingFaceDocBuilderDev]

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[merveenoyan]

Indeed reproduced this, thank you!