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kerasformers/
vgg16_tv_in1k

Name: vgg16_tv_in1k
Rating: 2.5 (41 reviews)

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Run VGG with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

kerasformers/vgg16_tv_in1k

Paper: Very Deep Convolutional Networks for Large-Scale Image Recognition (arXiv:1409.1556) · HF Papers

VGG is a classic deep CNN with stacked 3x3 convolutions. Available with and without BatchNorm as ImageNet classifiers / backbones.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of timm/vgg16.tv_in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (VGGImageClassify / VGGModel).

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
import numpy as np
from kerasformers.models.vgg import VGGImageClassify, VGGModel

model = VGGImageClassify.from_weights("kerasformers/vgg16_tv_in1k")
backbone = VGGModel.from_weights(
    "kerasformers/vgg16_tv_in1k", as_backbone=True
)

image = Image.open("your_image.jpg").convert("RGB")
image = image.resize((224, 224))
x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
print(model(x).shape)  # (1, num_classes)
feats = backbone(x)
print(len(feats), [tuple(f.shape) for f in feats])

Load any VGG variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
`vgg11_bn_tv_in1k`	`kerasformers/vgg11_bn_tv_in1k`
`vgg11_tv_in1k`	`kerasformers/vgg11_tv_in1k`
`vgg13_bn_tv_in1k`	`kerasformers/vgg13_bn_tv_in1k`
`vgg13_tv_in1k`	`kerasformers/vgg13_tv_in1k`
`vgg16_bn_tv_in1k`	`kerasformers/vgg16_bn_tv_in1k`
`vgg16_tv_in1k`	`kerasformers/vgg16_tv_in1k`
`vgg19_bn_tv_in1k`	`kerasformers/vgg19_bn_tv_in1k`
`vgg19_tv_in1k`	`kerasformers/vgg19_tv_in1k`

Tips

Set KERAS_BACKEND before importing Keras / kerasformers.
VGGImageClassify returns class logits; VGGModel returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: VGGImageClassify.from_weights("hf:timm/vgg16.tv_in1k").

Special Thanks

A huge thank you to the VGG authors and the timm / Hub communities for creating and releasing these models.

License: see YAML license (usually matches the upstream checkpoint).