Tensorflow keras computer vision attention models. Alias kecam. https://github.com/leondgarse/keras_cv_attention_models

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Project description

Keras_cv_attention_models

WARNING: currently NOT compatible with keras 3.0, if using tensorflow>=2.16.0, needs to install pip install tf-keras~=2.16 manually.
coco_train_script.py is under testing. Still struggling for this...
RepViT architecture is changed adapting new weights since kecam>1.3.22

>>>> Roadmap and todo list <<<<
General Usage
Recognition Models
- AotNet
- BEiT
- BEiTV2
- BotNet
- CAFormer
- CMT
- CoaT
- CoAtNet
- ConvNeXt
- ConvNeXtV2
- CoTNet
- CSPNeXt
- DaViT
- DiNAT
- DINOv2
- EdgeNeXt
- EfficientFormer
- EfficientFormerV2
- EfficientNet
- EfficientNetEdgeTPU
- EfficientNetV2
- EfficientViT_B
- EfficientViT_M
- EVA
- EVA02
- FasterNet
- FasterViT
- FastViT
- FBNetV3
- FlexiViT
- GCViT
- GhostNet
- GhostNetV2
- GMLP
- GPViT
- HaloNet
- Hiera
- HorNet
- IFormer
- InceptionNeXt
- LCNet
- LeViT
- MaxViT
- MetaTransFormer
- MLP mixer
- MobileNetV3
- MobileViT
- MobileViT_V2
- MogaNet
- NAT
- NFNets
- PVT_V2
- RegNetY
- RegNetZ
- RepViT
- ResMLP
- ResNeSt
- ResNetD
- ResNetQ
- ResNeXt
- SwinTransformerV2
- TinyNet
- TinyViT
- UniFormer
- VanillaNet
- VOLO
- WaveMLP
Detection Models
- EfficientDet
- YOLO_NAS
- YOLOR
- YOLOV7
- YOLOV8
- YOLOX
Language Models
- GPT2
- LLaMA2
Stable Diffusion
Segment Anything
Licenses
Citing

General Usage

Basic

Currently recommended TF version is tensorflow==2.11.1. Expecially for training or TFLite conversion.

Default import will not specific these while using them in READMEs.

import os
import sys
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow import keras

Install as pip package. kecam is a short alias name of this package. Note: the pip package kecam doesn't set any backend requirement, make sure either Tensorflow or PyTorch installed before hand. For PyTorch backend usage, refer Keras PyTorch Backend.
```
pip install -U kecam
# Or
pip install -U keras-cv-attention-models
# Or
pip install -U git+https://github.com/leondgarse/keras_cv_attention_models
```
Refer to each sub directory for detail usage.

Basic model prediction

from keras_cv_attention_models import volo
mm = volo.VOLO_d1(pretrained="imagenet")

""" Run predict """
import tensorflow as tf
from tensorflow import keras
from keras_cv_attention_models.test_images import cat
img = cat()
imm = keras.applications.imagenet_utils.preprocess_input(img, mode='torch')
pred = mm(tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)).numpy()
pred = tf.nn.softmax(pred).numpy()  # If classifier activation is not softmax
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.99664897),
#  ('n02123045', 'tabby', 0.0007249644),
#  ('n02123159', 'tiger_cat', 0.00020345),
#  ('n02127052', 'lynx', 5.4973923e-05),
#  ('n02123597', 'Siamese_cat', 2.675306e-05)]

Or just use model preset preprocess_input and decode_predictions

from keras_cv_attention_models import coatnet
mm = coatnet.CoAtNet0()

from keras_cv_attention_models.test_images import cat
preds = mm(mm.preprocess_input(cat()))
print(mm.decode_predictions(preds))
# [[('n02124075', 'Egyptian_cat', 0.9999875), ('n02123045', 'tabby', 5.194884e-06), ...]]

The preset preprocess_input and decode_predictions also compatible with PyTorch backend.

os.environ['KECAM_BACKEND'] = 'torch'

from keras_cv_attention_models import caformer
mm = caformer.CAFormerS18()
# >>>> Using PyTorch backend
# >>>> Aligned input_shape: [3, 224, 224]
# >>>> Load pretrained from: ~/.keras/models/caformer_s18_224_imagenet.h5

from keras_cv_attention_models.test_images import cat
preds = mm(mm.preprocess_input(cat()))
print(preds.shape)
# torch.Size([1, 1000])
print(mm.decode_predictions(preds))
# [[('n02124075', 'Egyptian_cat', 0.8817097), ('n02123045', 'tabby', 0.009335292), ...]]

num_classes=0 set for excluding model top GlobalAveragePooling2D + Dense layers.

from keras_cv_attention_models import resnest
mm = resnest.ResNest50(num_classes=0)
print(mm.output_shape)
# (None, 7, 7, 2048)

num_classes={custom output classes} others than 1000 or 0 will just skip loading the header Dense layer weights. As model.load_weights(weight_file, by_name=True, skip_mismatch=True) is used for loading weights.

from keras_cv_attention_models import swin_transformer_v2

mm = swin_transformer_v2.SwinTransformerV2Tiny_window8(num_classes=64)
# >>>> Load pretrained from: ~/.keras/models/swin_transformer_v2_tiny_window8_256_imagenet.h5
# WARNING:tensorflow:Skipping loading weights for layer #601 (named predictions) due to mismatch in shape for weight predictions/kernel:0. Weight expects shape (768, 64). Received saved weight with shape (768, 1000)
# WARNING:tensorflow:Skipping loading weights for layer #601 (named predictions) due to mismatch in shape for weight predictions/bias:0. Weight expects shape (64,). Received saved weight with shape (1000,)

Reload own model weights by set pretrained="xxx.h5". Better than calling model.load_weights directly, if reloading model with different input_shape and with weights shape not matching.

import os
from keras_cv_attention_models import coatnet
pretrained = os.path.expanduser('~/.keras/models/coatnet0_224_imagenet.h5')
mm = coatnet.CoAtNet1(input_shape=(384, 384, 3), pretrained=pretrained)  # No sense, just showing usage

Alias name kecam can be used instead of keras_cv_attention_models. It's __init__.py only with from keras_cv_attention_models import *.

import kecam
mm = kecam.yolor.YOLOR_CSP()
imm = kecam.test_images.dog_cat()
preds = mm(mm.preprocess_input(imm))
bboxs, lables, confidences = mm.decode_predictions(preds)[0]
kecam.coco.show_image_with_bboxes(imm, bboxs, lables, confidences)

Calculate flops method from TF 2.0 Feature: Flops calculation #32809. For PyTorch backend, needs thop pip install thop.

from keras_cv_attention_models import coatnet, resnest, model_surgery

model_surgery.get_flops(coatnet.CoAtNet0())
# >>>> FLOPs: 4,221,908,559, GFLOPs: 4.2219G
model_surgery.get_flops(resnest.ResNest50())
# >>>> FLOPs: 5,378,399,992, GFLOPs: 5.3784G

[Deprecated] tensorflow_addons is not imported by default. While reloading model depending on GroupNormalization like MobileViTV2 from h5 directly, needs to import tensorflow_addons manually first.
```
import tensorflow_addons as tfa

model_path = os.path.expanduser('~/.keras/models/mobilevit_v2_050_256_imagenet.h5')
mm = keras.models.load_model(model_path)
```

Export TF model to onnx. Needs tf2onnx for TF, pip install onnx tf2onnx onnxsim onnxruntime. For using PyTorch backend, exporting onnx is supported by PyTorch.

from keras_cv_attention_models import volo, nat, model_surgery
mm = nat.DiNAT_Small(pretrained=True)
model_surgery.export_onnx(mm, fuse_conv_bn=True, batch_size=1, simplify=True)
# Exported simplified onnx: dinat_small.onnx

# Run test
from keras_cv_attention_models.imagenet import eval_func
aa = eval_func.ONNXModelInterf(mm.name + '.onnx')
inputs = np.random.uniform(size=[1, *mm.input_shape[1:]]).astype('float32')
print(f"{np.allclose(aa(inputs), mm(inputs), atol=1e-5) = }")
# np.allclose(aa(inputs), mm(inputs), atol=1e-5) = True

Model summary model_summary.csv contains gathered model info.

params for model params count in M
flops for FLOPs in G
input for model input shape
acc_metrics means Imagenet Top1 Accuracy for recognition models, COCO val AP for detection models
inference_qps for T4 inference query per second with batch_size=1 + trtexec
extra means if any extra training info.

from keras_cv_attention_models import plot_func
plot_series = [
    "efficientnetv2", 'tinynet', 'lcnet', 'mobilenetv3', 'fasternet', 'fastervit', 'ghostnet',
    'inceptionnext', 'efficientvit_b', 'mobilevit', 'convnextv2', 'efficientvit_m', 'hiera',
]
plot_func.plot_model_summary(
    plot_series, model_table="model_summary.csv", log_scale_x=True, allow_extras=['mae_in1k_ft1k']
)

Code format is using line-length=160:

find ./* -name "*.py" | grep -v __init__ | xargs -I {} black -l 160 {}

T4 Inference

T4 Inference in the model tables are tested using trtexec on Tesla T4 with CUDA=12.0.1-1, Driver=525.60.13. All models are exported as ONNX using PyTorch backend, using batch_szie=1 only. Note: this data is for reference only, and vary in different batch sizes or benchmark tools or platforms or implementations.
All results are tested using colab trtexec.ipynb. Thus reproducible by any others.

os.environ["KECAM_BACKEND"] = "torch"

from keras_cv_attention_models import convnext, test_images, imagenet
# >>>> Using PyTorch backend
mm = convnext.ConvNeXtTiny()
mm.export_onnx(simplify=True)
# Exported onnx: convnext_tiny.onnx
# Running onnxsim.simplify...
# Exported simplified onnx: convnext_tiny.onnx

# Onnx run test
tt = imagenet.eval_func.ONNXModelInterf('convnext_tiny.onnx')
print(mm.decode_predictions(tt(mm.preprocess_input(test_images.cat()))))
# [[('n02124075', 'Egyptian_cat', 0.880507), ('n02123045', 'tabby', 0.0047998047), ...]]

""" Run trtexec benchmark """
!trtexec --onnx=convnext_tiny.onnx --fp16 --allowGPUFallback --useSpinWait --useCudaGraph

Layers

attention_layers is __init__.py only, which imports core layers defined in model architectures. Like RelativePositionalEmbedding from botnet, outlook_attention from volo, and many other Positional Embedding Layers / Attention Blocks.

from keras_cv_attention_models import attention_layers
aa = attention_layers.RelativePositionalEmbedding()
print(f"{aa(tf.ones([1, 4, 14, 16, 256])).shape = }")
# aa(tf.ones([1, 4, 14, 16, 256])).shape = TensorShape([1, 4, 14, 16, 14, 16])

Model surgery

model_surgery including functions used to change model parameters after built.

from keras_cv_attention_models import model_surgery
mm = keras.applications.ResNet50()  # Trainable params: 25,583,592

# Replace all ReLU with PReLU. Trainable params: 25,606,312
mm = model_surgery.replace_ReLU(mm, target_activation='PReLU')

# Fuse conv and batch_norm layers. Trainable params: 25,553,192
mm = model_surgery.convert_to_fused_conv_bn_model(mm)

ImageNet training and evaluating

ImageNet contains more detail usage and some comparing results.
Init Imagenet dataset using tensorflow_datasets #9.
For custom dataset, custom_dataset_script.py can be used creating a json format file, which can be used as --data_name xxx.json for training, detail usage can be found in Custom recognition dataset.
Another method creating custom dataset is using tfds.load, refer Writing custom datasets and Creating private tensorflow_datasets from tfds #48 by @Medicmind.
Running an AWS Sagemaker estimator job using keras_cv_attention_models can be found in AWS Sagemaker script example by @Medicmind.
aotnet.AotNet50 default parameters set is a typical ResNet50 architecture with Conv2D use_bias=False and padding like PyTorch.

Default parameters for train_script.py is like A3 configuration from ResNet strikes back: An improved training procedure in timm with batch_size=256, input_shape=(160, 160).

# `antialias` is default enabled for resize, can be turned off be set `--disable_antialias`.
CUDA_VISIBLE_DEVICES='0' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 train_script.py --seed 0 -s aotnet50

# Evaluation using input_shape (224, 224).
# `antialias` usage should be same with training.
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m aotnet50_epoch_103_val_acc_0.7674.h5 -i 224 --central_crop 0.95
# >>>> Accuracy top1: 0.78466 top5: 0.94088

Restore from break point by setting --restore_path and --initial_epoch, and keep other parameters same. restore_path is higher priority than model and additional_model_kwargs, also restore optimizer and loss. initial_epoch is mainly for learning rate scheduler. If not sure where it stopped, check checkpoints/{save_name}_hist.json.

import json
with open("checkpoints/aotnet50_hist.json", "r") as ff:
    aa = json.load(ff)
len(aa['lr'])
# 41 ==> 41 epochs are finished, initial_epoch is 41 then, restart from epoch 42

CUDA_VISIBLE_DEVICES='0' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 train_script.py --seed 0 -r checkpoints/aotnet50_latest.h5 -I 41
# >>>> Restore model from: checkpoints/aotnet50_latest.h5
# Epoch 42/105

eval_script.py is used for evaluating model accuracy. EfficientNetV2 self tested imagenet accuracy #19 just showing how different parameters affecting model accuracy.

# evaluating pretrained builtin model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m regnet.RegNetZD8
# evaluating pretrained timm model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m timm.models.resmlp_12_224 --input_shape 224

# evaluating specific h5 model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m checkpoints/xxx.h5
# evaluating specific tflite model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m xxx.tflite

Progressive training refer to PDF 2104.00298 EfficientNetV2: Smaller Models and Faster Training. AotNet50 A3 progressive input shapes 96 128 160:

CUDA_VISIBLE_DEVICES='1' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 progressive_train_script.py \
--progressive_epochs 33 66 -1 \
--progressive_input_shapes 96 128 160 \
--progressive_magnitudes 2 4 6 \
-s aotnet50_progressive_3_lr_steps_100 --seed 0

Transfer learning with freeze_backbone or freeze_norm_layers: EfficientNetV2B0 transfer learning on cifar10 testing freezing backbone #55.
Token label train test on CIFAR10 #57. Currently not working as well as expected. Token label is implementation of Github zihangJiang/TokenLabeling, paper PDF 2104.10858 All Tokens Matter: Token Labeling for Training Better Vision Transformers.

COCO training and evaluating

Currently still under testing.
COCO contains more detail usage.
custom_dataset_script.py can be used creating a json format file, which can be used as --data_name xxx.json for training, detail usage can be found in Custom detection dataset.
Default parameters for coco_train_script.py is EfficientDetD0 with input_shape=(256, 256, 3), batch_size=64, mosaic_mix_prob=0.5, freeze_backbone_epochs=32, total_epochs=105. Technically, it's any pyramid structure backbone + EfficientDet / YOLOX header / YOLOR header + anchor_free / yolor / efficientdet anchors combination supported.
Currently 4 types anchors supported, parameter anchors_mode controls which anchor to use, value in ["efficientdet", "anchor_free", "yolor", "yolov8"]. Default None for det_header presets.

NOTE: YOLOV8 has a default regression_len=64 for bbox output length. Typically it's 4 for other detection models, for yolov8 it's reg_max=16 -> regression_len = 16 * 4 == 64.

anchors_mode	use_object_scores	num_anchors	anchor_scale	aspect_ratios	num_scales	grid_zero_start
efficientdet	False	9	4	[1, 2, 0.5]	3	False
anchor_free	True	1	1	[1]	1	True
yolor	True	3	None	presets	None	offset=0.5
yolov8	False	1	1	[1]	1	False

# Default EfficientDetD0
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py
# Default EfficientDetD0 using input_shape 512, optimizer adamw, freezing backbone 16 epochs, total 50 + 5 epochs
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py -i 512 -p adamw --freeze_backbone_epochs 16 --lr_decay_steps 50

# EfficientNetV2B0 backbone + EfficientDetD0 detection header
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone efficientnet.EfficientNetV2B0 --det_header efficientdet.EfficientDetD0
# ResNest50 backbone + EfficientDetD0 header using yolox like anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone resnest.ResNest50 --anchors_mode anchor_free
# UniformerSmall32 backbone + EfficientDetD0 header using yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone uniformer.UniformerSmall32 --anchors_mode yolor

# Typical YOLOXS with anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolox.YOLOXS --freeze_backbone_epochs 0
# YOLOXS with efficientdet anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolox.YOLOXS --anchors_mode efficientdet --freeze_backbone_epochs 0
# CoAtNet0 backbone + YOLOX header with yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone coatnet.CoAtNet0 --det_header yolox.YOLOX --anchors_mode yolor

# Typical YOLOR_P6 with yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolor.YOLOR_P6 --freeze_backbone_epochs 0
# YOLOR_P6 with anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolor.YOLOR_P6 --anchors_mode anchor_free  --freeze_backbone_epochs 0
# ConvNeXtTiny backbone + YOLOR header with efficientdet anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone convnext.ConvNeXtTiny --det_header yolor.YOLOR --anchors_mode yolor

Note: COCO training still under testing, may change parameters and default behaviors. Take the risk if would like help developing.

coco_eval_script.py is used for evaluating model AP / AR on COCO validation set. It has a dependency pip install pycocotools which is not in package requirements. More usage can be found in COCO Evaluation.

# EfficientDetD0 using resize method bilinear w/o antialias
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m efficientdet.EfficientDetD0 --resize_method bilinear --disable_antialias
# >>>> [COCOEvalCallback] input_shape: (512, 512), pyramid_levels: [3, 7], anchors_mode: efficientdet

# YOLOX using BGR input format
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m yolox.YOLOXTiny --use_bgr_input --nms_method hard --nms_iou_or_sigma 0.65
# >>>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: anchor_free

# YOLOR / YOLOV7 using letterbox_pad and other tricks.
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m yolor.YOLOR_CSP --nms_method hard --nms_iou_or_sigma 0.65 \
--nms_max_output_size 300 --nms_topk -1 --letterbox_pad 64 --input_shape 704
# >>>> [COCOEvalCallback] input_shape: (704, 704), pyramid_levels: [3, 5], anchors_mode: yolor

# Specify h5 model
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m checkpoints/yoloxtiny_yolor_anchor.h5
# >>>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: yolor

[Experimental] Training using PyTorch backend

import os, sys, torch
os.environ["KECAM_BACKEND"] = "torch"

from keras_cv_attention_models.yolov8 import train, yolov8
from keras_cv_attention_models import efficientnet

global_device = torch.device("cuda:0") if torch.cuda.is_available() and int(os.environ.get("CUDA_VISIBLE_DEVICES", "0")) >= 0 else torch.device("cpu")
# model Trainable params: 7,023,904, GFLOPs: 8.1815G
bb = efficientnet.EfficientNetV2B0(input_shape=(3, 640, 640), num_classes=0)
model = yolov8.YOLOV8_N(backbone=bb, classifier_activation=None, pretrained=None).to(global_device)  # Note: classifier_activation=None
# model = yolov8.YOLOV8_N(input_shape=(3, None, None), classifier_activation=None, pretrained=None).to(global_device)
ema = train.train(model, dataset_path="coco.json", initial_epoch=0)

yolov8_training

CLIP training and evaluating

CLIP contains more detail usage.
custom_dataset_script.py can be used creating a tsv / json format file, which can be used as --data_name xxx.tsv for training, detail usage can be found in Custom caption dataset.

Train using clip_train_script.py on COCO captions Default --data_path is a testing one datasets/coco_dog_cat/captions.tsv.

CUDA_VISIBLE_DEVICES=1 TF_XLA_FLAGS="--tf_xla_auto_jit=2" python clip_train_script.py -i 160 -b 128 \
--text_model_pretrained None --data_path coco_captions.tsv

Train Using PyTorch backend by setting KECAM_BACKEND='torch'

KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python clip_train_script.py -i 160 -b 128 \
--text_model_pretrained None --data_path coco_captions.tsv

Text training

Currently it's only a simple one modified from Github karpathy/nanoGPT.

Train using text_train_script.py As dataset is randomly sampled, needs to specify steps_per_epoch

CUDA_VISIBLE_DEVICES=1 TF_XLA_FLAGS="--tf_xla_auto_jit=2" python text_train_script.py -m LLaMA2_15M \
--steps_per_epoch 8000 --batch_size 8 --tokenizer SentencePieceTokenizer

Train Using PyTorch backend by setting KECAM_BACKEND='torch'

KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python text_train_script.py -m LLaMA2_15M \
--steps_per_epoch 8000 --batch_size 8 --tokenizer SentencePieceTokenizer

Plotting

from keras_cv_attention_models import plot_func
hists = ['checkpoints/text_llama2_15m_tensorflow_hist.json', 'checkpoints/text_llama2_15m_torch_hist.json']
plot_func.plot_hists(hists, addition_plots=['val_loss', 'lr'], skip_first=3)

DDPM training

Stable Diffusion contains more detail usage.
Note: Works better with PyTorch backend, Tensorflow one seems overfitted if training logger like --epochs 200, and evaluation runs ~5 times slower. [???]

Dataset can be a directory containing images for basic DDPM training using images only, or a recognition json file created following Custom recognition dataset, which will train using labels as instruction.

python custom_dataset_script.py --train_images cifar10/train/ --test_images cifar10/test/
# >>>> total_train_samples: 50000, total_test_samples: 10000, num_classes: 10
# >>>> Saved to: cifar10.json

Train using ddpm_train_script.py on cifar10 with labels Default --data_path is builtin cifar10.

# Set --eval_interval 50 as TF evaluation is rather slow [???]
TF_XLA_FLAGS="--tf_xla_auto_jit=2" CUDA_VISIBLE_DEVICES=1 python ddpm_train_script.py --eval_interval 50

Train Using PyTorch backend by setting KECAM_BACKEND='torch'

KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python ddpm_train_script.py

Visualizing

Visualizing is for visualizing convnet filters or attention map scores.

make_and_apply_gradcam_heatmap is for Grad-CAM class activation visualization.

from keras_cv_attention_models import visualizing, test_images, resnest
mm = resnest.ResNest50()
img = test_images.dog()
superimposed_img, heatmap, preds = visualizing.make_and_apply_gradcam_heatmap(mm, img, layer_name="auto")

plot_attention_score_maps is model attention score maps visualization.

from keras_cv_attention_models import visualizing, test_images, botnet
img = test_images.dog()
_ = visualizing.plot_attention_score_maps(botnet.BotNetSE33T(), img)

TFLite Conversion

Currently TFLite not supporting tf.image.extract_patches / tf.transpose with len(perm) > 4. Some operations could be supported in latest or tf-nightly version, like previously not supported gelu / Conv2D with groups>1 are working now. May try if encountering issue.
More discussion can be found Converting a trained keras CV attention model to TFLite #17. Some speed testing results can be found How to speed up inference on a quantized model #44.
Functions like model_surgery.convert_groups_conv2d_2_split_conv2d and model_surgery.convert_gelu_to_approximate are not needed using up-to-date TF version.
Not supporting VOLO / HaloNet models converting, cause they need a longer tf.transpose perm.

model_surgery.convert_dense_to_conv converts all Dense layer with 3D / 4D inputs to Conv1D / Conv2D, as currently TFLite xnnpack not supporting it.

from keras_cv_attention_models import beit, model_surgery, efficientformer, mobilevit

mm = efficientformer.EfficientFormerL1()
mm = model_surgery.convert_dense_to_conv(mm)  # Convert all Dense layers
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())

Model	Dense, use_xnnpack=false	Conv, use_xnnpack=false	Conv, use_xnnpack=true
MobileViT_S	Inference (avg) 215371 us	Inference (avg) 163836 us	Inference (avg) 163817 us
EfficientFormerL1	Inference (avg) 126829 us	Inference (avg) 107053 us	Inference (avg) 107132 us

model_surgery.convert_extract_patches_to_conv converts tf.image.extract_patches to a Conv2D version:

from keras_cv_attention_models import cotnet, model_surgery
from keras_cv_attention_models.imagenet import eval_func

mm = cotnet.CotNetSE50D()
mm = model_surgery.convert_groups_conv2d_2_split_conv2d(mm)
# mm = model_surgery.convert_gelu_to_approximate(mm)  # Not required if using up-to-date TFLite
mm = model_surgery.convert_extract_patches_to_conv(mm)
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())
test_inputs = np.random.uniform(size=[1, *mm.input_shape[1:]])
print(np.allclose(mm(test_inputs), eval_func.TFLiteModelInterf(mm.name + '.tflite')(test_inputs), atol=1e-7))
# True

model_surgery.prepare_for_tflite is just a combination of above functions:

from keras_cv_attention_models import beit, model_surgery

mm = beit.BeitBasePatch16()
mm = model_surgery.prepare_for_tflite(mm)
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())

Detection models including efficinetdet / yolox / yolor, model can be converted a TFLite format directly. If need DecodePredictions also included in TFLite model, need to set use_static_output=True for DecodePredictions, as TFLite requires a more static output shape. Model output shape will be fixed as [batch, max_output_size, 6]. The last dimension 6 means [bbox_top, bbox_left, bbox_bottom, bbox_right, label_index, confidence], and those valid ones are where confidence > 0.

""" Init model """
from keras_cv_attention_models import efficientdet
model = efficientdet.EfficientDetD0(pretrained="coco")

""" Create a model with DecodePredictions using `use_static_output=True` """
model.decode_predictions.use_static_output = True
# parameters like score_threshold / iou_or_sigma can be set another value if needed.
nn = model.decode_predictions(model.outputs[0], score_threshold=0.5)
bb = keras.models.Model(model.inputs[0], nn)

""" Convert TFLite """
converter = tf.lite.TFLiteConverter.from_keras_model(bb)
open(bb.name + ".tflite", "wb").write(converter.convert())

""" Inference test """
from keras_cv_attention_models.imagenet import eval_func
from keras_cv_attention_models import test_images

dd = eval_func.TFLiteModelInterf(bb.name + ".tflite")
imm = test_images.cat()
inputs = tf.expand_dims(tf.image.resize(imm, dd.input_shape[1:-1]), 0)
inputs = keras.applications.imagenet_utils.preprocess_input(inputs, mode='torch')
preds = dd(inputs)[0]
print(f"{preds.shape = }")
# preds.shape = (100, 6)

pred = preds[preds[:, -1] > 0]
bboxes, labels, confidences = pred[:, :4], pred[:, 4], pred[:, -1]
print(f"{bboxes = }, {labels = }, {confidences = }")
# bboxes = array([[0.22825494, 0.47238672, 0.816262  , 0.8700745 ]], dtype=float32),
# labels = array([16.], dtype=float32),
# confidences = array([0.8309707], dtype=float32)

""" Show result """
from keras_cv_attention_models.coco import data
data.show_image_with_bboxes(imm, bboxes, labels, confidences, num_classes=90)

Using PyTorch as backend

Experimental Keras PyTorch Backend.
Set os environment export KECAM_BACKEND='torch' to enable this PyTorch backend.
Currently supports most recognition and detection models except hornet*gf / nfnets / volo. For detection models, using torchvision.ops.nms while running prediction.

Basic model build and prediction.

Will load same h5 weights as TF one if available.
Note: input_shape will auto fit image data format. Given input_shape=(224, 224, 3) or input_shape=(3, 224, 224), will both set to (3, 224, 224) if channels_first.
Note: model is default set to eval mode.

os.environ['KECAM_BACKEND'] = 'torch'
from keras_cv_attention_models import res_mlp
mm = res_mlp.ResMLP12()
# >>>> Load pretrained from: ~/.keras/models/resmlp12_imagenet.h5
print(f"{mm.input_shape = }")
# mm.input_shape = [None, 3, 224, 224]

import torch
print(f"{isinstance(mm, torch.nn.Module) = }")
# isinstance(mm, torch.nn.Module) = True

# Run prediction
from keras_cv_attention_models.test_images import cat
print(mm.decode_predictions(mm(mm.preprocess_input(cat())))[0])
# [('n02124075', 'Egyptian_cat', 0.9597896), ('n02123045', 'tabby', 0.012809471), ...]

Export typical PyTorch onnx / pth.

import torch
torch.onnx.export(mm, torch.randn(1, 3, *mm.input_shape[2:]), mm.name + ".onnx")

# Or by export_onnx
mm.export_onnx()
# Exported onnx: resmlp12.onnx

mm.export_pth()
# Exported pth: resmlp12.pth

Save weights as h5. This h5 can also be loaded in typical TF backend model. Currently it's only weights without model structure supported.
```
mm.save_weights("foo.h5")
```

Training with compile and fit Note: loss function arguments should be y_true, y_pred, while typical torch loss functions using y_pred, y_true.

import torch
from keras_cv_attention_models.backend import models, layers
mm = models.Sequential([layers.Input([3, 32, 32]), layers.Conv2D(32, 3), layers.GlobalAveragePooling2D(), layers.Dense(10)])
if torch.cuda.is_available():
    _ = mm.to("cuda")
xx = torch.rand([64, *mm.input_shape[1:]])
yy = torch.functional.F.one_hot(torch.randint(0, mm.output_shape[-1], size=[64]), mm.output_shape[-1]).float()
loss = lambda y_true, y_pred: (y_true - y_pred.float()).abs().mean()
# Here using `train_compile` instead of `compile`, as `compile` is already took by `nn.Module`.
mm.train_compile(optimizer="AdamW", loss=loss, metrics='acc', grad_accumulate=4)
mm.fit(xx, yy, epochs=2, batch_size=4)

Using keras core as backend

[Experimental] Set os environment export KECAM_BACKEND='keras_core' to enable this keras_core backend. Not using keras>3.0, as still not compiling with TensorFlow==2.15.0
keras-core has its own backends, supporting tensorflow / torch / jax, by editting ~/.keras/keras.json "backend" value.
Currently most recognition models except HaloNet / BotNet supported, also GPT2 / LLaMA2 supported.

Basic model build and prediction.

!pip install sentencepiece  # required for llama2 tokenizer
os.environ['KECAM_BACKEND'] = 'keras_core'
os.environ['KERAS_BACKEND'] = 'jax'
import kecam
print(f"{kecam.backend.backend() = }")
# kecam.backend.backend() = 'jax'
mm = kecam.llama2.LLaMA2_42M()
# >>>> Load pretrained from: ~/.keras/models/llama2_42m_tiny_stories.h5
mm.run_prediction('As evening fell, a maiden stood at the edge of a wood. In her hands,')
# >>>> Load tokenizer from file: ~/.keras/datasets/llama_tokenizer.model
# <s>
# As evening fell, a maiden stood at the edge of a wood. In her hands, she held a beautiful diamond. Everyone was surprised to see it.
# "What is it?" one of the kids asked.
# "It's a diamond," the maiden said.
# ...

Recognition Models

AotNet

Keras AotNet is just a ResNet / ResNetV2 like framework, that set parameters like attn_types and se_ratio and others, which is used to apply different types attention layer. Works like byoanet / byobnet from timm.
Default parameters set is a typical ResNet architecture with Conv2D use_bias=False and padding like PyTorch.

from keras_cv_attention_models import aotnet
# Mixing se and outlook and halo and mhsa and cot_attention, 21M parameters.
# 50 is just a picked number that larger than the relative `num_block`.
attn_types = [None, "outlook", ["bot", "halo"] * 50, "cot"],
se_ratio = [0.25, 0, 0, 0],
model = aotnet.AotNet50V2(attn_types=attn_types, se_ratio=se_ratio, stem_type="deep", strides=1)
model.summary()

BEiT

Keras BEiT includes models from PDF 2106.08254 BEiT: BERT Pre-Training of Image Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
BeitBasePatch16, 21k_ft1k	86.53M	17.61G	224	85.240	321.226 qps
- 21k_ft1k, 384	86.74M	55.70G	384	86.808	164.705 qps
BeitLargePatch16, 21k_ft1k	304.43M	61.68G	224	87.476	105.998 qps
- 21k_ft1k, 384	305.00M	191.65G	384	88.382	45.7307 qps
- 21k_ft1k, 512	305.67M	363.46G	512	88.584	21.3097 qps

BEiTV2

Keras BEiT includes models from BeitV2 Paper PDF 2208.06366 BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
BeitV2BasePatch16	86.53M	17.61G	224	85.5	322.52 qps
- 21k_ft1k	86.53M	17.61G	224	86.5	322.52 qps
BeitV2LargePatch16	304.43M	61.68G	224	87.3	105.734 qps
- 21k_ft1k	304.43M	61.68G	224	88.4	105.734 qps

BotNet

Keras BotNet is for PDF 2101.11605 Bottleneck Transformers for Visual Recognition.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
BotNet50	21M	5.42G	224		746.454 qps
BotNet101	41M	9.13G	224		448.102 qps
BotNet152	56M	12.84G	224		316.671 qps
BotNet26T	12.5M	3.30G	256	79.246	1188.84 qps
BotNextECA26T	10.59M	2.45G	256	79.270	1038.19 qps
BotNetSE33T	13.7M	3.89G	256	81.2	610.429 qps

CAFormer

Keras CAFormer is for PDF 2210.13452 MetaFormer Baselines for Vision. CAFormer is using 2 transformer stacks, while ConvFormer is all conv blocks.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
CAFormerS18	26M	4.1G	224	83.6	399.127 qps
- 384	26M	13.4G	384	85.0	181.993 qps
- 21k_ft1k	26M	4.1G	224	84.1	399.127 qps
- 21k_ft1k, 384	26M	13.4G	384	85.4	181.993 qps
CAFormerS36	39M	8.0G	224	84.5	204.328 qps
- 384	39M	26.0G	384	85.7	102.04 qps
- 21k_ft1k	39M	8.0G	224	85.8	204.328 qps
- 21k_ft1k, 384	39M	26.0G	384	86.9	102.04 qps
CAFormerM36	56M	13.2G	224	85.2	162.257 qps
- 384	56M	42.0G	384	86.2	65.6188 qps
- 21k_ft1k	56M	13.2G	224	86.6	162.257 qps
- 21k_ft1k, 384	56M	42.0G	384	87.5	65.6188 qps
CAFormerB36	99M	23.2G	224	85.5	116.865 qps
- 384	99M	72.2G	384	86.4	50.0244 qps
- 21k_ft1k	99M	23.2G	224	87.4	116.865 qps
- 21k_ft1k, 384	99M	72.2G	384	88.1	50.0244 qps

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ConvFormerS18	27M	3.9G	224	83.0	295.114 qps
- 384	27M	11.6G	384	84.4	145.923 qps
- 21k_ft1k	27M	3.9G	224	83.7	295.114 qps
- 21k_ft1k, 384	27M	11.6G	384	85.0	145.923 qps
ConvFormerS36	40M	7.6G	224	84.1	161.609 qps
- 384	40M	22.4G	384	85.4	80.2101 qps
- 21k_ft1k	40M	7.6G	224	85.4	161.609 qps
- 21k_ft1k, 384	40M	22.4G	384	86.4	80.2101 qps
ConvFormerM36	57M	12.8G	224	84.5	130.161 qps
- 384	57M	37.7G	384	85.6	63.9712 qps
- 21k_ft1k	57M	12.8G	224	86.1	130.161 qps
- 21k_ft1k, 384	57M	37.7G	384	86.9	63.9712 qps
ConvFormerB36	100M	22.6G	224	84.8	98.0751 qps
- 384	100M	66.5G	384	85.7	48.5897 qps
- 21k_ft1k	100M	22.6G	224	87.0	98.0751 qps
- 21k_ft1k, 384	100M	66.5G	384	87.6	48.5897 qps

CMT

Keras CMT is for PDF 2107.06263 CMT: Convolutional Neural Networks Meet Vision Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
CMTTiny, (Self trained 105 epochs)	9.5M	0.65G	160	77.4	315.566 qps
- (305 epochs)	9.5M	0.65G	160	78.94	315.566 qps
- 224, (fine-tuned 69 epochs)	9.5M	1.32G	224	80.73	254.87 qps
CMTTiny_torch, (1000 epochs)	9.5M	0.65G	160	79.2	338.207 qps
CMTXS_torch	15.2M	1.58G	192	81.8	241.288 qps
CMTSmall_torch	25.1M	4.09G	224	83.5	171.109 qps
CMTBase_torch	45.7M	9.42G	256	84.5	103.34 qps

CoaT

Keras CoaT is for PDF 2104.06399 CoaT: Co-Scale Conv-Attentional Image Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
CoaTLiteTiny	5.7M	1.60G	224	77.5	450.27 qps
CoaTLiteMini	11M	2.00G	224	79.1	452.884 qps
CoaTLiteSmall	20M	3.97G	224	81.9	248.846 qps
CoaTTiny	5.5M	4.33G	224	78.3	152.495 qps
CoaTMini	10M	6.78G	224	81.0	124.845 qps

CoAtNet

Keras CoAtNet is for PDF 2106.04803 CoAtNet: Marrying Convolution and Attention for All Data Sizes.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
CoAtNet0, 160, (105 epochs)	23.3M	2.09G	160	80.48	584.059 qps
CoAtNet0, (305 epochs)	23.8M	4.22G	224	82.79	400.333 qps
CoAtNet0	25M	4.6G	224	82.0	400.333 qps
- use_dw_strides=False	25M	4.2G	224	81.6	461.197 qps
CoAtNet1	42M	8.8G	224	83.5	206.954 qps
- use_dw_strides=False	42M	8.4G	224	83.3	228.938 qps
CoAtNet2	75M	16.6G	224	84.1	156.359 qps
- use_dw_strides=False	75M	15.7G	224	84.1	165.846 qps
CoAtNet2, 21k_ft1k	75M	16.6G	224	87.1	156.359 qps
CoAtNet3	168M	34.7G	224	84.5	95.0703 qps
CoAtNet3, 21k_ft1k	168M	34.7G	224	87.6	95.0703 qps
CoAtNet3, 21k_ft1k	168M	203.1G	512	87.9	95.0703 qps
CoAtNet4, 21k_ft1k	275M	360.9G	512	88.1	74.6022 qps
CoAtNet4, 21k_ft1k, PT-RA-E150	275M	360.9G	512	88.56	74.6022 qps

ConvNeXt

Keras ConvNeXt is for PDF 2201.03545 A ConvNet for the 2020s.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ConvNeXtTiny	28M	4.49G	224	82.1	361.58 qps
- 21k_ft1k	28M	4.49G	224	82.9	361.58 qps
- 21k_ft1k, 384	28M	13.19G	384	84.1	182.134 qps
ConvNeXtSmall	50M	8.73G	224	83.1	202.007 qps
- 21k_ft1k	50M	8.73G	224	84.6	202.007 qps
- 21k_ft1k, 384	50M	25.67G	384	85.8	108.125 qps
ConvNeXtBase	89M	15.42G	224	83.8	160.036 qps
- 384	89M	45.32G	384	85.1	83.3095 qps
- 21k_ft1k	89M	15.42G	224	85.8	160.036 qps
- 21k_ft1k, 384	89M	45.32G	384	86.8	83.3095 qps
ConvNeXtLarge	198M	34.46G	224	84.3	102.27 qps
- 384	198M	101.28G	384	85.5	47.2086 qps
- 21k_ft1k	198M	34.46G	224	86.6	102.27 qps
- 21k_ft1k, 384	198M	101.28G	384	87.5	47.2086 qps
ConvNeXtXlarge, 21k_ft1k	350M	61.06G	224	87.0	40.5776 qps
- 21k_ft1k, 384	350M	179.43G	384	87.8	21.797 qps
ConvNeXtXXLarge, clip	846M	198.09G	256	88.6

ConvNeXtV2

Keras ConvNeXt includes implementation of PDF 2301.00808 ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ConvNeXtV2Atto	3.7M	0.55G	224	76.7	705.822 qps
ConvNeXtV2Femto	5.2M	0.78G	224	78.5	728.02 qps
ConvNeXtV2Pico	9.1M	1.37G	224	80.3	591.502 qps
ConvNeXtV2Nano	15.6M	2.45G	224	81.9	471.918 qps
- 21k_ft1k	15.6M	2.45G	224	82.1	471.918 qps
- 21k_ft1k, 384	15.6M	7.21G	384	83.4	213.802 qps
ConvNeXtV2Tiny	28.6M	4.47G	224	83.0	301.982 qps
- 21k_ft1k	28.6M	4.47G	224	83.9	301.982 qps
- 21k_ft1k, 384	28.6M	13.1G	384	85.1	139.578 qps
ConvNeXtV2Base	89M	15.4G	224	84.9	132.575 qps
- 21k_ft1k	89M	15.4G	224	86.8	132.575 qps
- 21k_ft1k, 384	89M	45.2G	384	87.7	66.5729 qps
ConvNeXtV2Large	198M	34.4G	224	85.8	86.8846 qps
- 21k_ft1k	198M	34.4G	224	87.3	86.8846 qps
- 21k_ft1k, 384	198M	101.1G	384	88.2	24.4542 qps
ConvNeXtV2Huge	660M	115G	224	86.3
- 21k_ft1k	660M	337.9G	384	88.7
- 21k_ft1k, 384	660M	600.8G	512	88.9

CoTNet

Keras CoTNet is for PDF 2107.12292 Contextual Transformer Networks for Visual Recognition.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
CotNet50	22.2M	3.25G	224	81.3	324.913 qps
CotNetSE50D	23.1M	4.05G	224	81.6	513.077 qps
CotNet101	38.3M	6.07G	224	82.8	183.824 qps
CotNetSE101D	40.9M	8.44G	224	83.2	251.487 qps
CotNetSE152D	55.8M	12.22G	224	84.0	175.469 qps
CotNetSE152D	55.8M	24.92G	320	84.6	175.469 qps

CSPNeXt

Keras CSPNeXt is for backbone of PDF 2212.07784 RTMDet: An Empirical Study of Designing Real-Time Object Detectors.

Model	Params	FLOPs	Input	Top1 Acc
CSPNeXtTiny	2.73M	0.34G	224	69.44
CSPNeXtSmall	4.89M	0.66G	224	74.41
CSPNeXtMedium	13.05M	1.92G	224	79.27
CSPNeXtLarge	27.16M	4.19G	224	81.30
CSPNeXtXLarge	48.85M	7.75G	224	82.10

DaViT

Keras DaViT is for PDF 2204.03645 DaViT: Dual Attention Vision Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
DaViT_T	28.36M	4.56G	224	82.8	224.563 qps
DaViT_S	49.75M	8.83G	224	84.2	145.838 qps
DaViT_B	87.95M	15.55G	224	84.6	114.527 qps
DaViT_L, 21k_ft1k	196.8M	103.2G	384	87.5	34.7015 qps
DaViT_H, 1.5B	348.9M	327.3G	512	90.2	12.363 qps
DaViT_G, 1.5B	1.406B	1.022T	512	90.4

DiNAT

Keras DiNAT is for PDF 2209.15001 Dilated Neighborhood Attention Transformer.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
DiNAT_Mini	20.0M	2.73G	224	81.8	83.9943 qps
DiNAT_Tiny	27.9M	4.34G	224	82.7	61.1902 qps
DiNAT_Small	50.7M	7.84G	224	83.8	41.0343 qps
DiNAT_Base	89.8M	13.76G	224	84.4	30.1332 qps
DiNAT_Large, 21k_ft1k	200.9M	30.58G	224	86.6	18.4936 qps
- 21k, (num_classes=21841)	200.9M	30.58G	224
- 21k_ft1k, 384	200.9M	89.86G	384	87.4
DiNAT_Large_K11, 21k_ft1k	201.1M	92.57G	384	87.5

DINOv2

Keras DINOv2 includes models from PDF 2304.07193 DINOv2: Learning Robust Visual Features without Supervision.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
DINOv2_ViT_Small14	22.83M	47.23G	518	81.1	165.271 qps
DINOv2_ViT_Base14	88.12M	152.6G	518	84.5	54.9769 qps
DINOv2_ViT_Large14	306.4M	509.6G	518	86.3	17.4108 qps
DINOv2_ViT_Giant14	1139.6M	1790.3G	518	86.5

EdgeNeXt

Keras EdgeNeXt is for PDF 2206.10589 EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EdgeNeXt_XX_Small	1.33M	266M	256	71.23	902.957 qps
EdgeNeXt_X_Small	2.34M	547M	256	74.96	638.346 qps
EdgeNeXt_Small	5.59M	1.27G	256	79.41	536.762 qps
- usi	5.59M	1.27G	256	81.07	536.762 qps
EdgeNeXt_Base	18.5M	3.86G	256	82.47	383.461 qps
- usi	18.5M	3.86G	256	83.31	383.461 qps
- 21k_ft1k	18.5M	3.86G	256	83.68	383.461 qps

EfficientFormer

Keras EfficientFormer is for PDF 2206.01191 EfficientFormer: Vision Transformers at MobileNet Speed.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientFormerL1, distill	12.3M	1.31G	224	79.2	1214.22 qps
EfficientFormerL3, distill	31.4M	3.95G	224	82.4	596.705 qps
EfficientFormerL7, distill	74.4M	9.79G	224	83.3	298.434 qps

EfficientFormerV2

Keras EfficientFormer includes implementation of PDF 2212.08059 Rethinking Vision Transformers for MobileNet Size and Speed.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientFormerV2S0, distill	3.60M	405.2M	224	76.2	1114.38 qps
EfficientFormerV2S1, distill	6.19M	665.6M	224	79.7	841.186 qps
EfficientFormerV2S2, distill	12.7M	1.27G	224	82.0	573.9 qps
EfficientFormerV2L, distill	26.3M	2.59G	224	83.5	377.224 qps

EfficientNet

Keras EfficientNet includes implementation of PDF 1911.04252 Self-training with Noisy Student improves ImageNet classification.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientNetV1B0	5.3M	0.39G	224	77.6	1129.93 qps
- NoisyStudent	5.3M	0.39G	224	78.8	1129.93 qps
EfficientNetV1B1	7.8M	0.70G	240	79.6	758.639 qps
- NoisyStudent	7.8M	0.70G	240	81.5	758.639 qps
EfficientNetV1B2	9.1M	1.01G	260	80.5	668.959 qps
- NoisyStudent	9.1M	1.01G	260	82.4	668.959 qps
EfficientNetV1B3	12.2M	1.86G	300	81.9	473.607 qps
- NoisyStudent	12.2M	1.86G	300	84.1	473.607 qps
EfficientNetV1B4	19.3M	4.46G	380	83.3	265.244 qps
- NoisyStudent	19.3M	4.46G	380	85.3	265.244 qps
EfficientNetV1B5	30.4M	10.40G	456	84.3	146.758 qps
- NoisyStudent	30.4M	10.40G	456	86.1	146.758 qps
EfficientNetV1B6	43.0M	19.29G	528	84.8	88.0369 qps
- NoisyStudent	43.0M	19.29G	528	86.4	88.0369 qps
EfficientNetV1B7	66.3M	38.13G	600	85.2	52.6616 qps
- NoisyStudent	66.3M	38.13G	600	86.9	52.6616 qps
EfficientNetV1L2, NoisyStudent	480.3M	477.98G	800	88.4

EfficientNetEdgeTPU

Keras EfficientNetEdgeTPU includes implementation of PDF 1911.04252 Self-training with Noisy Student improves ImageNet classification.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientNetEdgeTPUSmall	5.49M	1.79G	224	78.07	1459.38 qps
EfficientNetEdgeTPUMedium	6.90M	3.01G	240	79.25	1028.95 qps
EfficientNetEdgeTPULarge	10.59M	7.94G	300	81.32	527.034 qps

EfficientNetV2

Keras EfficientNet includes implementation of PDF 2104.00298 EfficientNetV2: Smaller Models and Faster Training.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientNetV2B0	7.1M	0.72G	224	78.7	1109.84 qps
- 21k_ft1k	7.1M	0.72G	224	77.55?	1109.84 qps
EfficientNetV2B1	8.1M	1.21G	240	79.8	842.372 qps
- 21k_ft1k	8.1M	1.21G	240	79.03?	842.372 qps
EfficientNetV2B2	10.1M	1.71G	260	80.5	762.865 qps
- 21k_ft1k	10.1M	1.71G	260	79.48?	762.865 qps
EfficientNetV2B3	14.4M	3.03G	300	82.1	548.501 qps
- 21k_ft1k	14.4M	3.03G	300	82.46?	548.501 qps
EfficientNetV2T	13.6M	3.18G	288	82.34	496.483 qps
EfficientNetV2T_GC	13.7M	3.19G	288	82.46	368.763 qps
EfficientNetV2S	21.5M	8.41G	384	83.9	344.109 qps
- 21k_ft1k	21.5M	8.41G	384	84.9	344.109 qps
EfficientNetV2M	54.1M	24.69G	480	85.2	145.346 qps
- 21k_ft1k	54.1M	24.69G	480	86.2	145.346 qps
EfficientNetV2L	119.5M	56.27G	480	85.7	85.6514 qps
- 21k_ft1k	119.5M	56.27G	480	86.9	85.6514 qps
EfficientNetV2XL, 21k_ft1k	206.8M	93.66G	512	87.2	55.141 qps

EfficientViT_B

Keras EfficientViT_B is for Paper PDF 2205.14756 EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientViT_B0	3.41M	0.12G	224	71.6 ?	1581.76 qps
EfficientViT_B1	9.10M	0.58G	224	79.4	943.587 qps
- 256	9.10M	0.78G	256	79.9	840.844 qps
- 288	9.10M	1.03G	288	80.4	680.088 qps
EfficientViT_B2	24.33M	1.68G	224	82.1	583.295 qps
- 256	24.33M	2.25G	256	82.7	507.187 qps
- 288	24.33M	2.92G	288	83.1	419.93 qps
EfficientViT_B3	48.65M	4.14G	224	83.5	329.764 qps
- 256	48.65M	5.51G	256	83.8	288.605 qps
- 288	48.65M	7.14G	288	84.2	229.992 qps
EfficientViT_L1	52.65M	5.28G	224	84.48	503.068 qps
EfficientViT_L2	63.71M	6.98G	224	85.05	396.255 qps
- 384	63.71M	20.7G	384	85.98	207.322 qps
EfficientViT_L3	246.0M	27.6G	224	85.814	174.926 qps
- 384	246.0M	81.6G	384	86.408	86.895 qps

EfficientViT_M

Keras EfficientViT_M is for Paper PDF 2305.07027 EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EfficientViT_M0	2.35M	79.4M	224	63.2	814.522 qps
EfficientViT_M1	2.98M	167M	224	68.4	948.041 qps
EfficientViT_M2	4.19M	201M	224	70.8	906.286 qps
EfficientViT_M3	6.90M	263M	224	73.4	758.086 qps
EfficientViT_M4	8.80M	299M	224	74.3	672.891 qps
EfficientViT_M5	12.47M	522M	224	77.1	577.254 qps

EVA

Keras EVA includes models from PDF 2211.07636 EVA: Exploring the Limits of Masked Visual Representation Learning at Scale.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EvaLargePatch14, 21k_ft1k	304.14M	61.65G	196	88.59	115.532 qps
- 21k_ft1k, 336	304.53M	191.55G	336	89.20	53.3467 qps
EvaGiantPatch14, clip	1012.6M	267.40G	224	89.10
- m30m	1013.0M	621.45G	336	89.57
- m30m	1014.4M	1911.61G	560	89.80

EVA02

Keras EVA02 includes models from PDF 2303.11331 EVA: EVA-02: A Visual Representation for Neon Genesis.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
EVA02TinyPatch14, mim_in22k_ft1k	5.76M	4.72G	336	80.658	320.123 qps
EVA02SmallPatch14, mim_in22k_ft1k	22.13M	15.57G	336	85.74	161.774 qps
EVA02BasePatch14, mim_in22k_ft22k_ft1k	87.12M	107.6G	448	88.692	34.3962 qps
EVA02LargePatch14, mim_m38m_ft22k_ft1k	305.08M	363.68G	448	90.054

FasterNet

Keras FasterNet includes implementation of PDF 2303.03667 Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks .

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
FasterNetT0	3.9M	0.34G	224	71.9	1890.83 qps
FasterNetT1	7.6M	0.85G	224	76.2	1788.16 qps
FasterNetT2	15.0M	1.90G	224	78.9	1353.12 qps
FasterNetS	31.1M	4.55G	224	81.3	818.814 qps
FasterNetM	53.5M	8.72G	224	83.0	436.383 qps
FasterNetL	93.4M	15.49G	224	83.5	319.809 qps

FasterViT

Keras FasterViT includes implementation of PDF 2306.06189 FasterViT: Fast Vision Transformers with Hierarchical Attention.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
FasterViT0	31.40M	3.51G	224	82.1	716.809 qps
FasterViT1	53.37M	5.52G	224	83.2	491.971 qps
FasterViT2	75.92M	9.00G	224	84.2	377.006 qps
FasterViT3	159.55M	18.75G	224	84.9	216.481 qps
FasterViT4	351.12M	41.57G	224	85.4	71.6303 qps
FasterViT5	957.52M	114.08G	224	85.6
FasterViT6, +.2	1360.33M	144.13G	224	85.8

FastViT

Keras FastViT includes implementation of PDF 2303.14189 FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
FastViT_T8	4.03M	0.65G	256	76.2	1020.29 qps
- distill	4.03M	0.65G	256	77.2	1020.29 qps
- deploy=True	3.99M	0.64G	256	76.2	1323.14 qps
FastViT_T12	7.55M	1.34G	256	79.3	734.867 qps
- distill	7.55M	1.34G	256	80.3	734.867 qps
- deploy=True	7.50M	1.33G	256	79.3	956.332 qps
FastViT_S12	9.47M	1.74G	256	79.9	666.669 qps
- distill	9.47M	1.74G	256	81.1	666.669 qps
- deploy=True	9.42M	1.74G	256	79.9	881.429 qps
FastViT_SA12	11.58M	1.88G	256	80.9	656.95 qps
- distill	11.58M	1.88G	256	81.9	656.95 qps
- deploy=True	11.54M	1.88G	256	80.9	833.011 qps
FastViT_SA24	21.55M	3.66G	256	82.7	371.84 qps
- distill	21.55M	3.66G	256	83.4	371.84 qps
- deploy=True	21.49M	3.66G	256	82.7	444.055 qps
FastViT_SA36	31.53M	5.44G	256	83.6	267.986 qps
- distill	31.53M	5.44G	256	84.2	267.986 qps
- deploy=True	31.44M	5.43G	256	83.6	325.967 qps
FastViT_MA36	44.07M	7.64G	256	83.9	211.928 qps
- distill	44.07M	7.64G	256	84.6	211.928 qps
- deploy=True	43.96M	7.63G	256	83.9	274.559 qps

FBNetV3

Keras FBNetV3 includes implementation of PDF 2006.02049 FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
FBNetV3B	5.57M	539.82M	256	79.15	713.882 qps
FBNetV3D	10.31M	665.02M	256	79.68	635.963 qps
FBNetV3G	16.62M	1379.30M	256	82.05	478.835 qps

FlexiViT

Keras FlexiViT includes models from PDF 2212.08013 FlexiViT: One Model for All Patch Sizes.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
FlexiViTSmall	22.06M	5.36G	240	82.53	744.578 qps
FlexiViTBase	86.59M	20.33G	240	84.66	301.948 qps
FlexiViTLarge	304.47M	71.09G	240	85.64	105.187 qps

GCViT

Keras GCViT includes implementation of PDF 2206.09959 Global Context Vision Transformers.

Model	Params	FLOPs	Input	Top1 Acc	Download
GCViT_XXTiny	12.0M	2.15G	224	79.9	337.7 qps
GCViT_XTiny	20.0M	2.96G	224	82.0	255.625 qps
GCViT_Tiny	28.2M	4.83G	224	83.5	174.553 qps
GCViT_Tiny2	34.5M	6.28G	224	83.7
GCViT_Small	51.1M	8.63G	224	84.3	131.577 qps
GCViT_Small2	68.6M	11.7G	224	84.8
GCViT_Base	90.3M	14.9G	224	85.0	105.845 qps
GCViT_Large	202.1M	32.8G	224	85.7
- 21k_ft1k	202.1M	32.8G	224	86.6
- 21k_ft1k, 384	202.9M	105.1G	384	87.4
- 21k_ft1k, 512	203.8M	205.1G	512	87.6

GhostNet

Keras GhostNet includes implementation of PDF 1911.11907 GhostNet: More Features from Cheap Operations.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
GhostNet_050	2.59M	42.6M	224	66.88	1272.25 qps
GhostNet_100	5.18M	141.7M	224	74.16	1167.4 qps
GhostNet_130	7.36M	227.7M	224	75.79	1024.49 qps
- ssld	7.36M	227.7M	224	79.38	1024.49 qps

GhostNetV2

Keras GhostNet includes implementation of PDF GhostNetV2: Enhance Cheap Operation with Long-Range Attention.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
GhostNetV2_100	6.12M	168.5M	224	75.3	797.088 qps
GhostNetV2_130	8.96M	271.1M	224	76.9	722.668 qps
GhostNetV2_160	12.39M	400.9M	224	77.8	572.268 qps

GMLP

Keras GMLP includes implementation of PDF 2105.08050 Pay Attention to MLPs.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
GMLPTiny16	6M	1.35G	224	72.3	234.187 qps
GMLPS16	20M	4.44G	224	79.6	138.363 qps
GMLPB16	73M	15.82G	224	81.6	77.816 qps

GPViT

Keras GPViT includes implementation of PDF 2212.06795 GPVIT: A HIGH RESOLUTION NON-HIERARCHICAL VISION TRANSFORMER WITH GROUP PROPAGATION.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
GPViT_L1	9.59M	6.15G	224	80.5	210.166 qps
GPViT_L2	24.2M	15.74G	224	83.4	139.656 qps
GPViT_L3	36.7M	23.54G	224	84.1	131.284 qps
GPViT_L4	75.5M	48.29G	224	84.3	94.1899 qps

HaloNet

Keras HaloNet is for PDF 2103.12731 Scaling Local Self-Attention for Parameter Efficient Visual Backbones.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
HaloNextECA26T	10.7M	2.43G	256	79.50	1028.93 qps
HaloNet26T	12.5M	3.18G	256	79.13	1096.79 qps
HaloNetSE33T	13.7M	3.55G	256	80.99	582.008 qps
HaloRegNetZB	11.68M	1.97G	224	81.042	575.961 qps
HaloNet50T	22.7M	5.29G	256	81.70	512.677 qps
HaloBotNet50T	22.6M	5.02G	256	82.0	431.616 qps

Hiera

Keras Hiera is for PDF 2306.00989 Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
HieraTiny, mae_in1k_ft1k	27.91M	4.93G	224	82.8	644.356 qps
HieraSmall, mae_in1k_ft1k	35.01M	6.44G	224	83.8	491.669 qps
HieraBase, mae_in1k_ft1k	51.52M	9.43G	224	84.5	351.542 qps
HieraBasePlus, mae_in1k_ft1k	69.90M	12.71G	224	85.2	291.446 qps
HieraLarge, mae_in1k_ft1k	213.74M	40.43G	224	86.1	111.042 qps
HieraHuge, mae_in1k_ft1k	672.78M	125.03G	224	86.9

HorNet

Keras HorNet is for PDF 2207.14284 HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
HorNetTiny	22.4M	4.01G	224	82.8	222.665 qps
HorNetTinyGF	23.0M	3.94G	224	83.0
HorNetSmall	49.5M	8.87G	224	83.8	166.998 qps
HorNetSmallGF	50.4M	8.77G	224	84.0
HorNetBase	87.3M	15.65G	224	84.2	133.842 qps
HorNetBaseGF	88.4M	15.51G	224	84.3
HorNetLarge	194.5M	34.91G	224	86.8	89.8254 qps
HorNetLargeGF	196.3M	34.72G	224	87.0
HorNetLargeGF	201.8M	102.0G	384	87.7

IFormer

Keras IFormer is for PDF 2205.12956 Inception Transformer.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
IFormerSmall	19.9M	4.88G	224	83.4	254.392 qps
- 384	20.9M	16.29G	384	84.6	128.98 qps
IFormerBase	47.9M	9.44G	224	84.6	147.868 qps
- 384	48.9M	30.86G	384	85.7	77.8391 qps
IFormerLarge	86.6M	14.12G	224	84.6	113.434 qps
- 384	87.7M	45.74G	384	85.8	60.0292 qps

InceptionNeXt

Keras InceptionNeXt is for PDF 2303.16900 InceptionNeXt: When Inception Meets ConvNeXt.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
InceptionNeXtTiny	28.05M	4.21G	224	82.3	606.527 qps
InceptionNeXtSmall	49.37M	8.39G	224	83.5	329.01 qps
InceptionNeXtBase	86.67M	14.88G	224	84.0	260.639 qps
- 384	86.67M	43.73G	384	85.2	142.888 qps

LCNet

Keras LCNet includes implementation of PDF 2109.15099 PP-LCNet: A Lightweight CPU Convolutional Neural Network.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
LCNet050	1.88M	46.02M	224	63.10	3107.89 qps
- ssld	1.88M	46.02M	224	66.10	3107.89 qps
LCNet075	2.36M	96.82M	224	68.82	3083.55 qps
LCNet100	2.95M	158.28M	224	72.10	2752.6 qps
- ssld	2.95M	158.28M	224	74.39	2752.6 qps
LCNet150	4.52M	338.05M	224	73.71	2250.69 qps
LCNet200	6.54M	585.35M	224	75.18	2028.31 qps
LCNet250	9.04M	900.16M	224	76.60	1686.7 qps
- ssld	9.04M	900.16M	224	80.82	1686.7 qps

LeViT

Keras LeViT is for PDF 2104.01136 LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
LeViT128S, distill	7.8M	0.31G	224	76.6	800.53 qps
LeViT128, distill	9.2M	0.41G	224	78.6	628.714 qps
LeViT192, distill	11M	0.66G	224	80.0	597.299 qps
LeViT256, distill	19M	1.13G	224	81.6	538.885 qps
LeViT384, distill	39M	2.36G	224	82.6	460.139 qps

MaxViT

Keras MaxViT is for PDF 2204.01697 MaxViT: Multi-Axis Vision Transformer.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MaxViT_Tiny	31M	5.6G	224	83.62	195.283 qps
- 384	31M	17.7G	384	85.24	92.5725 qps
- 512	31M	33.7G	512	85.72	52.6485 qps
MaxViT_Small	69M	11.7G	224	84.45	149.286 qps
- 384	69M	36.1G	384	85.74	61.5757 qps
- 512	69M	67.6G	512	86.19	34.7002 qps
MaxViT_Base	119M	24.2G	224	84.95	74.7351 qps
- 384	119M	74.2G	384	86.34	31.9028 qps
- 512	119M	138.5G	512	86.66	17.8139 qps
- imagenet21k	135M	24.2G	224		74.7351 qps
- 21k_ft1k, 384	119M	74.2G	384	88.24	31.9028 qps
- 21k_ft1k, 512	119M	138.5G	512	88.38	17.8139 qps
MaxViT_Large	212M	43.9G	224	85.17	58.0967 qps
- 384	212M	133.1G	384	86.40	24.1388 qps
- 512	212M	245.4G	512	86.70	13.063 qps
- imagenet21k	233M	43.9G	224		58.0967 qps
- 21k_ft1k, 384	212M	133.1G	384	88.32	24.1388 qps
- 21k_ft1k, 512	212M	245.4G	512	88.46	13.063 qps
MaxViT_XLarge, imagenet21k	507M	97.7G	224
- 21k_ft1k, 384	475M	293.7G	384	88.51
- 21k_ft1k, 512	475M	535.2G	512	88.70

MetaTransFormer

Keras MetaTransFormer includes models from PDF 2307.10802 Meta-Transformer: A Unified Framework for Multimodal Learning.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MetaTransformerBasePatch16, laion_2b	86.86M	55.73G	384	85.4	150.731 qps
MetaTransformerLargePatch14, laion_2b	304.53M	191.6G	336	88.1	50.1536 qps

MLP mixer

Keras MLP mixer includes implementation of PDF 2105.01601 MLP-Mixer: An all-MLP Architecture for Vision.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MLPMixerS32, JFT	19.1M	1.01G	224	68.70	488.839 qps
MLPMixerS16, JFT	18.5M	3.79G	224	73.83	451.962 qps
MLPMixerB32, JFT	60.3M	3.25G	224	75.53	247.629 qps
- sam	60.3M	3.25G	224	72.47	247.629 qps
MLPMixerB16	59.9M	12.64G	224	76.44	207.423 qps
- 21k_ft1k	59.9M	12.64G	224	80.64	207.423 qps
- sam	59.9M	12.64G	224	77.36	207.423 qps
- JFT	59.9M	12.64G	224	80.00	207.423 qps
MLPMixerL32, JFT	206.9M	11.30G	224	80.67	95.1865 qps
MLPMixerL16	208.2M	44.66G	224	71.76	77.9928 qps
- 21k_ft1k	208.2M	44.66G	224	82.89	77.9928 qps
- JFT	208.2M	44.66G	224	84.82	77.9928 qps
- 448	208.2M	178.54G	448	83.91
- 448, JFT	208.2M	178.54G	448	86.78
MLPMixerH14, JFT	432.3M	121.22G	224	86.32
- 448, JFT	432.3M	484.73G	448	87.94

MobileNetV3

Keras MobileNetV3 includes implementation of PDF 1905.02244 Searching for MobileNetV3.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MobileNetV3Small050	1.29M	24.92M	224	57.89	2458.28 qps
MobileNetV3Small075	2.04M	44.35M	224	65.24	2286.44 qps
MobileNetV3Small100	2.54M	57.62M	224	67.66	2058.06 qps
MobileNetV3Large075	3.99M	156.30M	224	73.44	1643.78 qps
MobileNetV3Large100	5.48M	218.73M	224	75.77	1629.44 qps
- miil	5.48M	218.73M	224	77.92	1629.44 qps

MobileViT

Keras MobileViT is for PDF 2110.02178 MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MobileViT_XXS	1.3M	0.42G	256	69.0	1319.43 qps
MobileViT_XS	2.3M	1.05G	256	74.7	1019.57 qps
MobileViT_S	5.6M	2.03G	256	78.3	790.943 qps

MobileViT_V2

Keras MobileViT_V2 is for PDF 2206.02680 Separable Self-attention for Mobile Vision Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MobileViT_V2_050	1.37M	0.47G	256	70.18	718.337 qps
MobileViT_V2_075	2.87M	1.04G	256	75.56	642.323 qps
MobileViT_V2_100	4.90M	1.83G	256	78.09	591.217 qps
MobileViT_V2_125	7.48M	2.84G	256	79.65	510.25 qps
MobileViT_V2_150	10.6M	4.07G	256	80.38	466.482 qps
- 21k_ft1k	10.6M	4.07G	256	81.46	466.482 qps
- 21k_ft1k, 384	10.6M	9.15G	384	82.60	278.834 qps
MobileViT_V2_175	14.3M	5.52G	256	80.84	412.759 qps
- 21k_ft1k	14.3M	5.52G	256	81.94	412.759 qps
- 21k_ft1k, 384	14.3M	12.4G	384	82.93	247.108 qps
MobileViT_V2_200	18.4M	7.12G	256	81.17	394.325 qps
- 21k_ft1k	18.4M	7.12G	256	82.36	394.325 qps
- 21k_ft1k, 384	18.4M	16.2G	384	83.41	229.399 qps

MogaNet

Keras MogaNet is for PDF 2211.03295 Efficient Multi-order Gated Aggregation Network.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
MogaNetXtiny	2.96M	806M	224	76.5	398.488 qps
MogaNetTiny	5.20M	1.11G	224	79.0	362.409 qps
- 256	5.20M	1.45G	256	79.6	335.372 qps
MogaNetSmall	25.3M	4.98G	224	83.4	249.807 qps
MogaNetBase	43.7M	9.96G	224	84.2	133.071 qps
MogaNetLarge	82.5M	15.96G	224	84.6	84.2045 qps

NAT

Keras NAT is for PDF 2204.07143 Neighborhood Attention Transformer.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
NAT_Mini	20.0M	2.73G	224	81.8	85.2324 qps
NAT_Tiny	27.9M	4.34G	224	83.2	62.6147 qps
NAT_Small	50.7M	7.84G	224	83.7	41.1545 qps
NAT_Base	89.8M	13.76G	224	84.3	30.8989 qps

NFNets

Keras NFNets is for PDF 2102.06171 High-Performance Large-Scale Image Recognition Without Normalization.

Model	Params	FLOPs	Input	Top1 Acc
NFNetL0	35.07M	7.13G	288	82.75
NFNetF0	71.5M	12.58G	256	83.6
NFNetF1	132.6M	35.95G	320	84.7
NFNetF2	193.8M	63.24G	352	85.1
NFNetF3	254.9M	115.75G	416	85.7
NFNetF4	316.1M	216.78G	512	85.9
NFNetF5	377.2M	291.73G	544	86.0
NFNetF6, sam	438.4M	379.75G	576	86.5
NFNetF7	499.5M	481.80G	608
ECA_NFNetL0	24.14M	7.12G	288	82.58
ECA_NFNetL1	41.41M	14.93G	320	84.01
ECA_NFNetL2	56.72M	30.12G	384	84.70
ECA_NFNetL3	72.04M	52.73G	448

PVT_V2

Keras PVT_V2 is for PDF 2106.13797 PVTv2: Improved Baselines with Pyramid Vision Transformer.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
PVT_V2B0	3.7M	580.3M	224	70.5	561.593 qps
PVT_V2B1	14.0M	2.14G	224	78.7	392.408 qps
PVT_V2B2	25.4M	4.07G	224	82.0	210.476 qps
PVT_V2B2_linear	22.6M	3.94G	224	82.1	226.791 qps
PVT_V2B3	45.2M	6.96G	224	83.1	135.51 qps
PVT_V2B4	62.6M	10.19G	224	83.6	97.666 qps
PVT_V2B5	82.0M	11.81G	224	83.8	81.4798 qps

RegNetY

Keras RegNetY is for PDF 2003.13678 Designing Network Design Spaces.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
RegNetY040	20.65M	3.98G	224	82.3	749.277 qps
RegNetY064	30.58M	6.36G	224	83.0	436.946 qps
RegNetY080	39.18M	7.97G	224	83.17	513.43 qps
RegNetY160	83.59M	15.92G	224	82.0	338.046 qps
RegNetY320	145.05M	32.29G	224	82.5	188.508 qps

RegNetZ

Keras RegNetZ includes implementation of Github timm/models/byobnet.py.
Related paper PDF 2004.02967 Evolving Normalization-Activation Layers

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
RegNetZB16	9.72M	1.44G	224	79.868	751.035 qps
RegNetZC16	13.46M	2.50G	256	82.164	636.549 qps
RegNetZC16_EVO	13.49M	2.55G	256	81.9
RegNetZD32	27.58M	5.96G	256	83.422	459.204 qps
RegNetZD8	23.37M	3.95G	256	83.5	460.021 qps
RegNetZD8_EVO	23.46M	4.61G	256	83.42
RegNetZE8	57.70M	9.88G	256	84.5	274.97 qps

RepViT

Keras RepViT is for PDF 2307.09283 RepViT: Revisiting Mobile CNN From ViT Perspective.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
RepViT_M09, distillation	5.10M	0.82G	224	79.1
- deploy=True	5.07M	0.82G	224	79.1	966.72 qps
RepViT_M10, distillation	6.85M	1.12G	224	80.3	1157.8 qps
- deploy=True	6.81M	1.12G	224	80.3
RepViT_M11, distillation	8.29M	1.35G	224	81.2	846.682 qps
- deploy=True	8.24M	1.35G	224	81.2	1027.5 qps
RepViT_M15, distillation	14.13M	2.30G	224	82.5
- deploy=True	14.05M	2.30G	224	82.5
RepViT_M23, distillation	23.01M	4.55G	224	83.7
- deploy=True	22.93M	4.55G	224	83.7

ResMLP

Keras ResMLP includes implementation of PDF 2105.03404 ResMLP: Feedforward networks for image classification with data-efficient training.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ResMLP12	15M	3.02G	224	77.8	928.402 qps
ResMLP24	30M	5.98G	224	80.8	420.709 qps
ResMLP36	116M	8.94G	224	81.1	309.513 qps
ResMLP_B24	129M	100.39G	224	83.6	78.3015 qps
- 21k_ft1k	129M	100.39G	224	84.4	78.3015 qps

ResNeSt

Keras ResNeSt is for PDF 2004.08955 ResNeSt: Split-Attention Networks.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ResNest50	28M	5.38G	224	81.03	534.627 qps
ResNest101	49M	13.33G	256	82.83	257.074 qps
ResNest200	71M	35.55G	320	83.84	118.183 qps
ResNest269	111M	77.42G	416	84.54	61.167 qps

ResNetD

Keras ResNetD includes implementation of PDF 1812.01187 Bag of Tricks for Image Classification with Convolutional Neural Networks

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ResNet50D	25.58M	4.33G	224	80.530	930.214 qps
ResNet101D	44.57M	8.04G	224	83.022	502.268 qps
ResNet152D	60.21M	11.75G	224	83.680	353.279 qps
ResNet200D	64.69M	15.25G	224	83.962	287.73 qps

ResNetQ

Keras ResNetQ includes implementation of Github timm/models/resnet.py

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ResNet51Q	35.7M	4.87G	224	82.36	838.754 qps
ResNet61Q	36.8M	5.96G	224		730.245 qps

ResNeXt

Keras ResNeXt includes implementation of PDF 1611.05431 Aggregated Residual Transformations for Deep Neural Networks.
SWSL means Semi-Weakly Supervised ResNe*t from Github facebookresearch/semi-supervised-ImageNet1K-models. Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
ResNeXt50, (32x4d)	25M	4.23G	224	79.768	1041.46 qps
- SWSL	25M	4.23G	224	82.182	1041.46 qps
ResNeXt50D, (32x4d + deep)	25M	4.47G	224	79.676	1010.94 qps
ResNeXt101, (32x4d)	42M	7.97G	224	80.334	571.652 qps
- SWSL	42M	7.97G	224	83.230	571.652 qps
ResNeXt101W, (32x8d)	89M	16.41G	224	79.308	367.431 qps
- SWSL	89M	16.41G	224	84.284	367.431 qps
ResNeXt101W_64, (64x4d)	83.46M	15.46G	224	82.46	377.83 qps

SwinTransformerV2

Keras SwinTransformerV2 includes implementation of PDF 2111.09883 Swin Transformer V2: Scaling Up Capacity and Resolution.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
SwinTransformerV2Tiny_ns	28.3M	4.69G	224	81.8	289.205 qps
SwinTransformerV2Small_ns	49.7M	9.12G	224	83.5	169.645 qps
SwinTransformerV2Tiny_window8	28.3M	5.99G	256	81.8	275.547 qps
SwinTransformerV2Tiny_window16	28.3M	6.75G	256	82.8	217.207 qps
SwinTransformerV2Small_window8	49.7M	11.63G	256	83.7	157.559 qps
SwinTransformerV2Small_window16	49.7M	12.93G	256	84.1	129.953 qps
SwinTransformerV2Base_window8	87.9M	20.44G	256	84.2	126.294 qps
SwinTransformerV2Base_window16	87.9M	22.17G	256	84.6	99.634 qps
SwinTransformerV2Base_window16, 21k_ft1k	87.9M	22.17G	256	86.2	99.634 qps
SwinTransformerV2Base_window24, 21k_ft1k	87.9M	55.89G	384	87.1	35.0508 qps
SwinTransformerV2Large_window16, 21k_ft1k	196.7M	48.03G	256	86.9
SwinTransformerV2Large_window24, 21k_ft1k	196.7M	117.1G	384	87.6

TinyNet

Keras TinyNet includes implementation of PDF 2010.14819 Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
TinyNetE	2.04M	25.22M	106	59.86	2152.36 qps
TinyNetD	2.34M	53.35M	152	66.96	1905.56 qps
TinyNetC	2.46M	103.22M	184	71.23	1353.44 qps
TinyNetB	3.73M	206.28M	188	74.98	1196.06 qps
TinyNetA	6.19M	343.74M	192	77.65	981.976 qps

TinyViT

Keras TinyViT includes implementation of PDF 2207.10666 TinyViT: Fast Pretraining Distillation for Small Vision Transformers.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
TinyViT_5M, distill	5.4M	1.3G	224	79.1	631.414 qps
- 21k_ft1k	5.4M	1.3G	224	80.7	631.414 qps
TinyViT_11M, distill	11M	2.0G	224	81.5	509.818 qps
- 21k_ft1k	11M	2.0G	224	83.2	509.818 qps
TinyViT_21M, distill	21M	4.3G	224	83.1	410.676 qps
- 21k_ft1k	21M	4.3G	224	84.8	410.676 qps
- 21k_ft1k, 384	21M	13.8G	384	86.2	199.458 qps
- 21k_ft1k, 512	21M	27.0G	512	86.5	122.846 qps

UniFormer

Keras UniFormer includes implementation of PDF 2201.09450 UniFormer: Unifying Convolution and Self-attention for Visual Recognition.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
UniformerSmall32, token_label	22M	3.66G	224	83.4	577.334 qps
UniformerSmall64	22M	3.66G	224	82.9	562.794 qps
- token_label	22M	3.66G	224	83.4	562.794 qps
UniformerSmallPlus32	24M	4.24G	224	83.4	546.82 qps
- token_label	24M	4.24G	224	83.9	546.82 qps
UniformerSmallPlus64	24M	4.23G	224	83.4	538.193 qps
- token_label	24M	4.23G	224	83.6	538.193 qps
UniformerBase32, token_label	50M	8.32G	224	85.1	272.485 qps
UniformerBase64	50M	8.31G	224	83.8	286.963 qps
- token_label	50M	8.31G	224	84.8	286.963 qps
UniformerLarge64, token_label	100M	19.79G	224	85.6	154.761 qps
- token_label, 384	100M	63.11G	384	86.3	75.3487 qps

VanillaNet

Keras VanillaNet is for PDF 2305.12972 VanillaNet: the Power of Minimalism in Deep Learning.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
VanillaNet5	22.33M	8.46G	224	72.49	598.964 qps
- deploy=True	15.52M	5.17G	224	72.49	798.199 qps
VanillaNet6	56.12M	10.11G	224	76.36	465.031 qps
- deploy=True	32.51M	6.00G	224	76.36	655.944 qps
VanillaNet7	56.67M	11.84G	224	77.98	375.479 qps
- deploy=True	32.80M	6.90G	224	77.98	527.723 qps
VanillaNet8	65.18M	13.50G	224	79.13	341.157 qps
- deploy=True	37.10M	7.75G	224	79.13	479.328 qps
VanillaNet9	73.68M	15.17G	224	79.87	312.815 qps
- deploy=True	41.40M	8.59G	224	79.87	443.464 qps
VanillaNet10	82.19M	16.83G	224	80.57	277.871 qps
- deploy=True	45.69M	9.43G	224	80.57	408.082 qps
VanillaNet11	90.69M	18.49G	224	81.08	267.026 qps
- deploy=True	50.00M	10.27G	224	81.08	377.239 qps
VanillaNet12	99.20M	20.16G	224	81.55	229.987 qps
- deploy=True	54.29M	11.11G	224	81.55	358.076 qps
VanillaNet13	107.7M	21.82G	224	82.05	218.256 qps
- deploy=True	58.59M	11.96G	224	82.05	334.244 qps

VOLO

Keras VOLO is for PDF 2106.13112 VOLO: Vision Outlooker for Visual Recognition.

Model	Params	FLOPs	Input	Top1 Acc
VOLO_d1	27M	4.82G	224	84.2
- 384	27M	14.22G	384	85.2
VOLO_d2	59M	9.78G	224	85.2
- 384	59M	28.84G	384	86.0
VOLO_d3	86M	13.80G	224	85.4
- 448	86M	55.50G	448	86.3
VOLO_d4	193M	29.39G	224	85.7
- 448	193M	117.81G	448	86.8
VOLO_d5	296M	53.34G	224	86.1
- 448	296M	213.72G	448	87.0
- 512	296M	279.36G	512	87.1

WaveMLP

Keras WaveMLP includes implementation of PDF 2111.12294 An Image Patch is a Wave: Quantum Inspired Vision MLP.

Model	Params	FLOPs	Input	Top1 Acc	T4 Inference
WaveMLP_T	17M	2.47G	224	80.9	523.4 qps
WaveMLP_S	30M	4.55G	224	82.9	203.445 qps
WaveMLP_M	44M	7.92G	224	83.3	147.155 qps
WaveMLP_B	63M	10.26G	224	83.6

Detection Models

EfficientDet

Keras EfficientDet includes implementation of Paper 1911.09070 EfficientDet: Scalable and Efficient Object Detection.
Det-AdvProp + AutoAugment Paper 2103.13886 Robust and Accurate Object Detection via Adversarial Learning.

Model	Params	FLOPs	Input	COCO val AP	test AP	T4 Inference
EfficientDetD0	3.9M	2.55G	512	34.3	34.6	248.009 qps
- Det-AdvProp	3.9M	2.55G	512	35.1	35.3	248.009 qps
EfficientDetD1	6.6M	6.13G	640	40.2	40.5	133.139 qps
- Det-AdvProp	6.6M	6.13G	640	40.8	40.9	133.139 qps
EfficientDetD2	8.1M	11.03G	768	43.5	43.9	89.0523 qps
- Det-AdvProp	8.1M	11.03G	768	44.3	44.3	89.0523 qps
EfficientDetD3	12.0M	24.95G	896	46.8	47.2	50.0498 qps
- Det-AdvProp	12.0M	24.95G	896	47.7	48.0	50.0498 qps
EfficientDetD4	20.7M	55.29G	1024	49.3	49.7	28.0086 qps
- Det-AdvProp	20.7M	55.29G	1024	50.4	50.4	28.0086 qps
EfficientDetD5	33.7M	135.62G	1280	51.2	51.5
- Det-AdvProp	33.7M	135.62G	1280	52.2	52.5
EfficientDetD6	51.9M	225.93G	1280	52.1	52.6
EfficientDetD7	51.9M	325.34G	1536	53.4	53.7
EfficientDetD7X	77.0M	410.87G	1536	54.4	55.1
EfficientDetLite0	3.2M	0.98G	320	27.5	26.41	599.616 qps
EfficientDetLite1	4.2M	1.97G	384	32.6	31.50	369.273 qps
EfficientDetLite2	5.3M	3.38G	448	36.2	35.06	278.263 qps
EfficientDetLite3	8.4M	7.50G	512	39.9	38.77	180.871 qps
EfficientDetLite3X	9.3M	14.01G	640	44.0	42.64	115.271 qps
EfficientDetLite4	15.1M	20.20G	640	44.4	43.18	95.4122 qps

YOLO_NAS

Keras YOLO_NAS includes implementation of Github Deci-AI/super-gradients YOLO-NAS models.

Model	Params	FLOPs	Input	COCO val AP	T4 Inference
YOLO_NAS_S	12.88M	16.96G	640	47.5	240.087 qps
- use_reparam_conv=False	12.18M	15.92G	640	47.5	345.595 qps
YOLO_NAS_M	33.86M	47.12G	640	51.55	128.96 qps
- use_reparam_conv=False	31.92M	43.91G	640	51.55	167.935 qps
YOLO_NAS_L	44.53M	64.53G	640	52.22	98.6069 qps
- use_reparam_conv=False	42.02M	59.95G	640	52.22	131.11 qps

YOLOR

Keras YOLOR includes implementation of Paper 2105.04206 You Only Learn One Representation: Unified Network for Multiple Tasks.

Model	Params	FLOPs	Input	COCO val AP	test AP	T4 Inference
YOLOR_CSP	52.9M	60.25G	640	50.0	52.8	118.746 qps
YOLOR_CSPX	99.8M	111.11G	640	51.5	54.8	67.9444 qps
YOLOR_P6	37.3M	162.87G	1280	52.5	55.7	49.3128 qps
YOLOR_W6	79.9M	226.67G	1280	53.6 ?	56.9	40.2355 qps
YOLOR_E6	115.9M	341.62G	1280	50.3 ?	57.6	21.5719 qps
YOLOR_D6	151.8M	467.88G	1280	50.8 ?	58.2	16.6061 qps

YOLOV7

Keras YOLOV7 includes implementation of Paper 2207.02696 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Model	Params	FLOPs	Input	COCO val AP	T4 Inference
YOLOV7_Tiny	6.23M	2.90G	416	33.3	845.903 qps
YOLOV7_CSP	37.67M	53.0G	640	51.4	137.441 qps
YOLOV7_X	71.41M	95.0G	640	53.1	82.0534 qps
YOLOV7_W6	70.49M	180.1G	1280	54.9	49.9841 qps
YOLOV7_E6	97.33M	257.6G	1280	56.0	31.3852 qps
YOLOV7_D6	133.9M	351.4G	1280	56.6	26.1346 qps
YOLOV7_E6E	151.9M	421.7G	1280	56.8	20.1331 qps

YOLOV8

Keras YOLOV8 includes implementation of Github ultralytics/ultralytics detection and classification models.

Model	Params	FLOPs	Input	COCO val AP	T4 Inference
YOLOV8_N	3.16M	4.39G	640	37.3	614.042 qps
YOLOV8_S	11.17M	14.33G	640	44.9	349.528 qps
YOLOV8_M	25.90M	39.52G	640	50.2	160.212 qps
YOLOV8_L	43.69M	82.65G	640	52.9	104.452 qps
YOLOV8_X	68.23M	129.0G	640	53.9	66.0428 qps
YOLOV8_X6	97.42M	522.6G	1280	56.7 ?	17.4368 qps

YOLOX

Keras YOLOX includes implementation of Paper 2107.08430 YOLOX: Exceeding YOLO Series in 2021.

Model	Params	FLOPs	Input	COCO val AP	test AP	T4 Inference
YOLOXNano	0.91M	0.53G	416	25.8		930.57 qps
YOLOXTiny	5.06M	3.22G	416	32.8		745.2 qps
YOLOXS	9.0M	13.39G	640	40.5	40.5	380.38 qps
YOLOXM	25.3M	36.84G	640	46.9	47.2	181.084 qps
YOLOXL	54.2M	77.76G	640	49.7	50.1	111.517 qps
YOLOXX	99.1M	140.87G	640	51.5	51.5	62.3189 qps

Language Models

GPT2

Keras GPT2 includes implementation of Language Models are Unsupervised Multitask Learners. T4 Inference is tested using input_shape=[1, 1024].

Model	Params	FLOPs	vocab_size	LAMBADA PPL	T4 Inference
GPT2_Base	163.04M	146.42G	50257	35.13	51.4483 qps
GPT2_Medium	406.29M	415.07G	50257	15.60	21.756 qps
GPT2_Large	838.36M	890.28G	50257	10.87
GPT2_XLarge, +.2	1.638B	1758.3G	50257	8.63

LLaMA2

Keras LLaMA2 includes implementation of PDF 2307.09288 Llama 2: Open Foundation and Fine-Tuned Chat Models.
tiny_stories weights ported from Github karpathy/llama2.c, and LLaMA2_1B model weights ported from Github jzhang38/TinyLlama TinyLlama-1.1B-Chat-V0.4 one.

Model	Params	FLOPs	vocab_size	Val loss
LLaMA2_15M	24.41M	4.06G	32000	1.072
LLaMA2_42M	58.17M	50.7G	32000	0.847
LLaMA2_110M	134.1M	130.2G	32000	0.760
LLaMA2_1B	1.10B	2.50T	32003
LLaMA2_7B	6.74B	14.54T	32000

Stable Diffusion

Keras Stable Diffusion includes implementation of PDF 2112.10752 High-Resolution Image Synthesis with Latent Diffusion Models. Weights ported from Github runwayml/stable-diffusion sd-v1-5.ckpt.

Model	Params	FLOPs	Input	Download
ViTTextLargePatch14	123.1M	6.67G	[None, 77]	vit_text_large_patch14_clip.h5
Encoder	34.16M	559.6G	[None, 512, 512, 3]	encoder_v1_5.h5
UNet	859.5M	404.4G	[None, 64, 64, 4]	unet_v1_5.h5
Decoder	49.49M	1259.5G	[None, 64, 64, 4]	decoder_v1_5.h5

Segment Anything

Keras Segment Anything includes implementation of PDF 2304.02643 Segment Anything.

Model	Params	FLOPs	Input	COCO val mIoU
MobileSAM	5.74M	39.4G	1024	72.8
TinySAM	5.74M	39.4G	1024
EfficientViT_SAM_L0	30.73M	35.4G	512	74.45

Licenses

This part is copied and modified according to Github rwightman/pytorch-image-models.
Code. The code here is licensed MIT. It is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue. So far all of the pretrained weights available here are pretrained on ImageNet and COCO with a select few that have some additional pretraining.
ImageNet Pretrained Weights. ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.
COCO Pretrained Weights. Should follow cocodataset termsofuse. The annotations in COCO dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License. The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.
Pretrained on more than ImageNet and COCO. Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.

Citing

BibTeX

@misc{leondgarse,
  author = {Leondgarse},
  title = {Keras CV Attention Models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.6506947},
  howpublished = {\url{https://github.com/leondgarse/keras_cv_attention_models}}
}

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keras-cv-attention-models 1.4.1

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Keras_cv_attention_models

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