kvmm

KVMM: Keras Vision Models 🚀

📌 Table of Contents

• 📖 Introduction
• ⚡ Installation
• 🛠️ Usage
• 📑 Models
• 📜 License
• 🌟 Credits

📖 Introduction

Keras Vision Models (KVMM) is a collection of vision models with pretrained weights, built entirely with Keras 3. It supports a range of tasks, including segmentation, object detection, vision-language modeling (VLMs), and classification. KVMM includes custom layers and backbone support, providing flexibility and efficiency across various vision applications. For backbones, there are various weight variants like in1k, in21k, fb_dist_in1k, ms_in22k, fb_in22k_ft_in1k, ns_jft_in1k, aa_in1k, cvnets_in1k, augreg_in21k_ft_in1k, augreg_in21k, and many more.

⚡Installation

From PyPI (recommended)

pip install -U kvmm

From Source

pip install -U git+https://github.com/IMvision12/keras-vision-models

🛠️ Usage

🔎 Listing Available Models

Shows all available models, including backbones, segmentation models, object detection models, and vision-language models (VLMs). It also includes the names of the weights available for each specific model variant.

import kvmm
print(kvmm.list_models())

## Output:
"""
CaiTM36 : fb_dist_in1k_384
CaiTM48 : fb_dist_in1k_448
CaiTS24 : fb_dist_in1k_224, fb_dist_in1k_384
...
ConvMixer1024D20 : in1k
ConvMixer1536D20 : in1k
...
ConvNeXtAtto : d2_in1k
ConvNeXtBase : fb_in1k, fb_in22k, fb_in22k_ft_in1k, fb_in22k_ft_in1k_384
...
"""

🔎 List Specific Model Variant

import kvmm
print(kvmm.list_models("swin"))

# Output:
"""
SwinBaseP4W12 : ms_in1k, ms_in22k, ms_in22k_ft_in1k
SwinBaseP4W7 : ms_in1k, ms_in22k, ms_in22k_ft_in1k
SwinLargeP4W12 : ms_in22k, ms_in22k_ft_in1k
SwinLargeP4W7 : ms_in22k, ms_in22k_ft_in1k
SwinSmallP4W7 : ms_in1k, ms_in22k, ms_in22k_ft_in1k
SwinTinyP4W7 : ms_in1k, ms_in22k
"""

⚙️ Layers

KVMM provides various custom layers like StochasticDepth, LayerScale, EfficientMultiheadSelfAttention, and more. These layers can be seamlessly integrated into your custom models and workflows 🚀

import kvmm

# Example 1
layer = kvmm.layers.StochasticDepth(drop_path_rate=0.1)
output = layer(input_tensor, training=True)

# Example 2
window_partition = WindowPartition(window_size=7)
windowed_features = window_partition(features, height=28, width=28)

🏗️ Backbone Usage (Classification)

🛠️ Basic Usage

import kvmm
import numpy as np

# default configuration
model = kvmm.models.vit.ViTTiny16()

# For Fine-Tuning (default weight)
model = kvmm.models.vit.ViTTiny16(include_top=False, input_shape=(224,224,3))
# Custom Weight
model = kvmm.models.vit.ViTTiny16(include_top=False, input_shape=(224,224,3), weights="augreg_in21k_224")

# Backbone Support
model = kvmm.models.vit.ViTTiny16(include_top=False, as_backbone=True, input_shape=(224,224,3), weights="augreg_in21k_224")
random_input = np.random.rand(1, 224, 224, 3).astype(np.float32)
features = model(random_input)
print(f"Number of feature maps: {len(features)}")
for i, feature in enumerate(features):
    print(f"Feature {i} shape: {feature.shape}")

"""
Output:

Number of feature maps: 13
Feature 0 shape: (1, 197, 192)
Feature 1 shape: (1, 197, 192)
Feature 2 shape: (1, 197, 192)
...
"""    

Example Inference

from keras import ops
from keras.applications.imagenet_utils import decode_predictions
import kvmm
from PIL import Image

model = kvmm.models.swin.SwinTinyP4W7(input_shape=[224, 224, 3])

image = Image.open("bird.png").resize((224, 224))
x = ops.convert_to_tensor(image)
x = ops.expand_dims(x, axis=0)

# Predict
preds = model.predict(x)
print("Predicted:", decode_predictions(preds, top=3)[0])

#output:
Predicted: [('n01537544', 'indigo_bunting', np.float32(0.9135666)), ('n01806143', 'peacock', np.float32(0.0003379386)), ('n02017213', 'European_gallinule', np.float32(0.00027174334))]

🧩 Segmentation

🛠️ Basic Usage

import kvmm

# Pre-Trained weights (cityscapes or ade20kor mit(in1k))
# ade20k and cityscapes can be used for fine-tuning by giving custom `num_classes`
# If `num_classes` is not specified by default for ade20k it will be 150 and for cityscapes it will be 19
model = kvmm.models.segformer.SegFormerB0(weights="ade20k", input_shape=(512,512,3))
model = kvmm.models.segformer.SegFormerB0(weights="cityscapes", input_shape=(512,512,3))

# Fine-Tune using `MiT` backbone (This will load `in1k` weights)
model = kvmm.models.segformer.SegFormerB0(weights="mit", input_shape=(512,512,3))

🚀 Custom Backbone Support

import kvmm

# With no backbone weights
backbone = kvmm.models.resnet.ResNet50(as_backbone=True, weights=None, include_top=False, input_shape=(224,224,3))
segformer = kvmm.models.segformer.SegFormerB0(weights=None, backbone=backbone, num_classes=10, input_shape=(224,224,3))

# With backbone weights
import kvmm
backbone = kvmm.models.resnet.ResNet50(as_backbone=True, weights="tv_in1k", include_top=False, input_shape=(224,224,3))
segformer = kvmm.models.segformer.SegFormerB0(weights=None, backbone=backbone, num_classes=10, input_shape=(224,224,3))

🚀 Example Inference

import kvmm
from PIL import Image
import numpy as np

model = kvmm.models.segformer.SegFormerB0(weights="ade20k_512")

image = Image.open("ADE_train_00000586.jpg")
processed_img = kvmm.models.segformer.SegFormerImageProcessor(image=image,
    do_resize=True,
    size={"height": 512, "width": 512},
    do_rescale=True,
    do_normalize=True)
outs = model.predict(processed_img)
outs = np.argmax(outs[0], axis=-1)
visualize_segmentation(outs, image)

Object Detection 🚧

VLMS 🚧

🛠️ Basic Usage

import keras

import kvmm

processor = kvmm.models.clip.CLIPProcessor()
model = kvmm.models.clip.ClipVitBase16(
    weights="res_224px",
    input_shape=(224, 224, 3),
)
inputs = processor(text=["mountains", "tortoise", "cat"], image_paths="cat1.jpg")
output = model(
    {
        "images": inputs["images"],
        "token_ids": inputs["input_ids"],
        "padding_mask": inputs["attention_mask"],
    }
)

print("Raw Model Output:")
print(output)

preds = keras.ops.softmax(output["image_logits"]).numpy().squeeze()
result = dict(zip(["mountains", "tortoise", "cat"], preds))
print("\nPrediction probabilities:")
print(result)

#output:
"""{'image_logits': <tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[11.042501, 10.388493, 18.414747]], dtype=float32)>, 'text_logits': <tf.Tensor: shape=(3, 1), dtype=float32, numpy=
array([[11.042501],
       [10.388493],
       [18.414747]], dtype=float32)>}

Prediction probabilities:
{'mountains': np.float32(0.0006278555), 'tortoise': np.float32(0.000326458), 'cat': np.float32(0.99904567)}"""

📑 Models

• Backbones:

  🏷️ Model Name	📜 Reference Paper	📦 Source of Weights
  CaiT	Going deeper with Image Transformers	timm
  ConvMixer	Patches Are All You Need?	timm
  ConvNeXt	A ConvNet for the 2020s	timm
  ConvNeXt V2	ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders	timm
  DeiT	Training data-efficient image transformers & distillation through attention	timm
  DenseNet	Densely Connected Convolutional Networks	timm
  EfficientNet	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	timm
  EfficientNet-Lite	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	timm
  EfficientNetV2	EfficientNetV2: Smaller Models and Faster Training	timm
  FlexiViT	FlexiViT: One Model for All Patch Sizes	timm
  InceptionNeXt	InceptionNeXt: When Inception Meets ConvNeXt	timm
  Inception-ResNet-v2	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	timm
  Inception-v3	Rethinking the Inception Architecture for Computer Vision	timm
  Inception-v4	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	timm
  MiT	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers	transformers
  MLP-Mixer	MLP-Mixer: An all-MLP Architecture for Vision	timm
  MobileNetV2	MobileNetV2: Inverted Residuals and Linear Bottlenecks	timm
  MobileNetV3	Searching for MobileNetV3	keras
  MobileViT	MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer	timm
  MobileViTV2	Separable Self-attention for Mobile Vision Transformers	timm
  PiT	Rethinking Spatial Dimensions of Vision Transformers	timm
  PoolFormer	MetaFormer is Actually What You Need for Vision	timm
  Res2Net	Res2Net: A New Multi-scale Backbone Architecture	timm
  ResMLP	ResMLP: Feedforward networks for image classification with data-efficient training	timm
  ResNet	Deep Residual Learning for Image Recognition	timm
  ResNetV2	Identity Mappings in Deep Residual Networks	timm
  ResNeXt	Aggregated Residual Transformations for Deep Neural Networks	timm
  SENet	Squeeze-and-Excitation Networks	timm
  Swin Transformer	Swin Transformer: Hierarchical Vision Transformer using Shifted Windows	timm
  VGG	Very Deep Convolutional Networks for Large-Scale Image Recognition	timm
  ViT	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	timm
  Xception	Xception: Deep Learning with Depthwise Separable Convolutions	keras

• Segmentation

  🏷️ Model Name	📜 Reference Paper	📦 Source of Weights
  SegFormer	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers	transformers

• Vision-Language-Models (VLMs)

  🏷️ Model Name	📜 Reference Paper	📦 Source of Weights
  CLIP	Learning Transferable Visual Models From Natural Language Supervision	transformers

📜 License

This project leverages timm and transformers for converting pretrained weights from PyTorch to Keras. For licensing details, please refer to the respective repositories.

• 🔖 kvmm Code: This repository is licensed under the Apache 2.0 License.

🌟 Credits

• The Keras team for their powerful and user-friendly deep learning framework
• The Transformers library for its robust tools for loading and adapting pretrained models
• The pytorch-image-models (timm) project for pioneering many computer vision model implementations
• All contributors to the original papers and architectures implemented in this library

Citing

BibTeX

@misc{gc2025kvmm,
  author = {Gitesh Chawda},
  title = {Keras Vision Models},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/IMvision12/keras-vision-models}}
}