resnet50.fb_swsl_ig1b_ft_in1k

198.0K
1.0B
license:cc-by-nc-4.0
by
timm
Image Model
OTHER
1B params
Good
198K downloads
Production-ready
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

--- license: cc-by-nc-4.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by resnet50.fb_swsl_ig1b_ft_in1k with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Model Usagepython
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('resnet50.fb_swsl_ig1b_ft_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
Model Usagepython
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('resnet50.fb_swsl_ig1b_ft_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

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