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Image Classification
timm
PyTorch
Safetensors
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metadata
license: apache-2.0
library_name: timm
tags:
  - image-classification
  - timm
datasets:
  - imagenet-1k
  - imagenet-22k

Model card for mvitv2_large_cls.fb_inw21k

A MViT-v2 (multi-scale ViT) image classification model. Pretrained on ImageNet-22k (Winter21 variant) and fine-tuned on ImageNet-1k by paper authors. The classifier layout for this model was not shared and does not match expected lexicographical sorted synset order.

Model Details

  • Model Type: Image classification / feature backbone
  • Model Stats:
    • Params (M): 234.6
    • GMACs: 42.2
    • Activations (M): 111.7
    • Image size: 224 x 224
  • Papers:
  • Dataset: ImageNet-1k
  • Pretrain Dataset: ImageNet-22k
  • Original: https://github.com/facebookresearch/mvit

Model Usage

Image Classification

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('mvitv2_large_cls.fb_inw21k', 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)

Image Embeddings

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(
    'mvitv2_large_cls.fb_inw21k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
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))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 50, 1152) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor