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

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License: Apache Software License (Apache 2.0)

Author: Leondgarse

Tags tensorflow, keras, cv, attention, pretrained, models, kecam

Requires: Python >=3.6

Classifiers

Project description

Keras_cv_attention_models

  • coco_train_script.py is under testing

Roadmap and todo list

General Usage

Basic

  • Currently recommended TF version is tensorflow==2.10.0. Expecially for training or TFLite conversion.
  • Default import 
    import os
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: 
    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 skimage.data import chelsea
img = chelsea() # Chelsea the 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', 0.9692954),
#  ('n02123045', 'tabby', 0.020203391),
#  ('n02123159', 'tiger_cat', 0.006867502),
#  ('n02127052', 'lynx', 0.00017674894),
#  ('n02123597', 'Siamese_cat', 4.9493494e-05)]
    Or just use model preset preprocess_input and decode_predictions 
    from keras_cv_attention_models import coatnet
from skimage.data import chelsea
mm = coatnet.CoAtNet0()
preds = mm(mm.preprocess_input(chelsea()))
print(mm.decode_predictions(preds))
# [[('n02124075', 'Egyptian_cat', 0.9653769), ('n02123159', 'tiger_cat', 0.018427467), ...]
  • 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,)
  • 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)
  • Reload own model weights by set pretrained="xxx.h5". Better 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)
  • Alias name kecam can be used instead of keras_cv_attention_models. It's __init__.py only with one line 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. 
    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
  • 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)
  • Code format is using line-length=160: 
    find ./* -name "*.py" | grep -v __init__ | grep -v setup.py | xargs -I {} black -l 160 {}

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, recommending method is using tfds.load, refer Writing custom datasets and Creating private tensorflow_datasets from tfds #48 by @Medicmind.
  • custom_dataset_script.py can also 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.
  • 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" ./train_script.py --seed 0 -s aotnet50

    # Evaluation using input_shape (224, 224).
# `antialias` usage should be same with training.
CUDA_VISIBLE_DEVICES='1' ./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
    aotnet50_imagenet
  • 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" ./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' ./eval_script.py -m regnet.RegNetZD8
# evaluating pretrained timm model
CUDA_VISIBLE_DEVICES='1' ./eval_script.py -m timm.models.resmlp_12_224 --input_shape 224

# evaluating specific h5 model
CUDA_VISIBLE_DEVICES='1' ./eval_script.py -m checkpoints/xxx.h5
# evaluating specific tflite model
CUDA_VISIBLE_DEVICES='1' ./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" ./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
    aotnet50_progressive_160
  • 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 3 types anchors supported, parameter anchors_mode controls which anchor to use, value in ["efficientdet", "anchor_free", "yolor"]. Default None for det_header presets.

    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 
    # Default EfficientDetD0
CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py
# Default EfficientDetD0 using input_shape 512, optimizer adamw, freezing backbone 16 epochs, total 50 + 5 epochs
CUDA_VISIBLE_DEVICES='0' ./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' ./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' ./coco_train_script.py --backbone resnest.ResNest50 --anchors_mode anchor_free
# UniformerSmall32 backbone + EfficientDetD0 header using yolor anchors
CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --backbone uniformer.UniformerSmall32 --anchors_mode yolor

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

# Typical YOLOR_P6 with yolor anchors
CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --det_header yolor.YOLOR_P6 --freeze_backbone_epochs 0
# YOLOR_P6 with anchor_free anchors
CUDA_VISIBLE_DEVICES='0' ./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' ./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' ./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' ./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 using letterbox_pad and other tricks.
CUDA_VISIBLE_DEVICES='1' ./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' ./coco_eval_script.py -m checkpoints/yoloxtiny_yolor_anchor.h5
# >>>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: yolor

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 Conv2D with groups>1 / gelu / tf.image.extract_patches / tf.transpose with len(perm) > 4. Some operations could be supported in tf-nightly version. 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.
  • tf.nn.gelu(inputs, approximate=True) activation works for TFLite. Define model with activation="gelu/approximate" or activation="gelu/app" will set approximate=True for gelu. Should better decide before training, or there may be accuracy loss.
  • model_surgery.convert_groups_conv2d_2_split_conv2d converts model Conv2D with groups>1 layers to SplitConv using split -> conv -> concat: 
    from keras_cv_attention_models import regnet, model_surgery
from keras_cv_attention_models.imagenet import eval_func

bb = regnet.RegNetZD32()
mm = model_surgery.convert_groups_conv2d_2_split_conv2d(bb)  # converts all `Conv2D` using `groups` to `SplitConv2D`
test_inputs = np.random.uniform(size=[1, *mm.input_shape[1:]])
print(np.allclose(mm(test_inputs), bb(test_inputs)))
# True

converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())
print(np.allclose(mm(test_inputs), eval_func.TFLiteModelInterf(mm.name + '.tflite')(test_inputs), atol=1e-7))
# True
  • model_surgery.convert_gelu_and_extract_patches_for_tflite converts model gelu activation to gelu approximate=True, and 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_and_extract_patches_for_tflite(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 2 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())
  • Not supporting VOLO / HaloNet models converting, cause they need a longer tf.transpose perm.

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

Model Params FLOPs Input Top1 Acc Download
BeitBasePatch16, 21k 86.53M 17.61G 224 85.240 beit_base_patch16_224.h5
86.74M 55.70G 384 86.808 beit_base_patch16_384.h5
BeitLargePatch16, 21k 304.43M 61.68G 224 87.476 beit_large_patch16_224.h5
305.00M 191.65G 384 88.382 beit_large_patch16_384.h5
305.67M 363.46G 512 88.584 beit_large_patch16_512.h5

BotNet

Model Params FLOPs Input Top1 Acc Download
BotNet50 21M 5.42G 224
BotNet101 41M 9.13G 224
BotNet152 56M 12.84G 224
BotNet26T 12.5M 3.30G 256 79.246 botnet26t_256_imagenet.h5
BotNextECA26T 10.59M 2.45G 256 79.270 botnext_eca26t_256_imagenet.h5
BotNetSE33T 13.7M 3.89G 256 81.2 botnet_se33t_256_imagenet.h5

CMT

Model Params FLOPs Input Top1 Acc Download
CMTTiny, (Self trained 105 epochs) 9.5M 0.65G 160 77.4
- 305 epochs 9.5M 0.65G 160 78.94 cmt_tiny_160_imagenet
- fine-tuned 224 (69 epochs) 9.5M 1.32G 224 80.73 cmt_tiny_224_imagenet
CMTTiny_torch, 1000 epochs 9.5M 0.65G 160 79.2 cmt_tiny_torch_160
CMTXS_torch 15.2M 1.58G 192 81.8 cmt_xs_torch_192
CMTSmall_torch 25.1M 4.09G 224 83.5 cmt_small_torch_224
CMTBase_torch 45.7M 9.42G 256 84.5 cmt_base_torch_256

CoaT

Model Params FLOPs Input Top1 Acc Download
CoaTLiteTiny 5.7M 1.60G 224 77.5 coat_lite_tiny_imagenet.h5
CoaTLiteMini 11M 2.00G 224 79.1 coat_lite_mini_imagenet.h5
CoaTLiteSmall 20M 3.97G 224 81.9 coat_lite_small_imagenet.h5
CoaTTiny 5.5M 4.33G 224 78.3 coat_tiny_imagenet.h5
CoaTMini 10M 6.78G 224 81.0 coat_mini_imagenet.h5

CoAtNet

Model Params FLOPs Input Top1 Acc Download
CoAtNet0 (Self trained 105 epochs) 23.3M 2.09G 160 80.48 coatnet0_160_imagenet.h5
- fine-tune 224, 37 epochs 23.3M 4.17G 224 82.21 coatnet0_224_imagenet.h5
CoAtNet0 25M 4.2G 224 81.6
CoAtNet0, Stride-2 DConv2D 25M 4.6G 224 82.0
CoAtNet1 42M 8.4G 224 83.3
CoAtNet1, Stride-2 DConv2D 42M 8.8G 224 83.5
CoAtNet2 75M 15.7G 224 84.1
CoAtNet2, Stride-2 DConv2D 75M 16.6G 224 84.1
CoAtNet2, ImageNet-21k pretrain 75M 16.6G 224 87.1
CoAtNet3 168M 34.7G 224 84.5
CoAtNet3, ImageNet-21k pretrain 168M 34.7G 224 87.6
CoAtNet3, ImageNet-21k pretrain 168M 203.1G 512 87.9
CoAtNet4, ImageNet-21k pretrain 275M 360.9G 512 88.1
CoAtNet4, ImageNet-21K + PT-RA-E150 275M 360.9G 512 88.56

JFT pre-trained models accuracy

Model Input Reported Params self-defined Params Top1 Acc
CoAtNet3, Stride-2 DConv2D 384 168M, FLOPs 114G 160.64M, FLOPs 109.67G 88.52
CoAtNet3, Stride-2 DConv2D 512 168M, FLOPs 214G 161.24M, FLOPs 205.06G 88.81
CoAtNet4 512 275M, FLOPs 361G 270.69M, FLOPs 359.77G 89.11
CoAtNet5 512 688M, FLOPs 812G 676.23M, FLOPs 807.06G 89.77
CoAtNet6 512 1.47B, FLOPs 1521G 1.336B, FLOPs 1470.56G 90.45
CoAtNet7 512 2.44B, FLOPs 2586G 2.413B, FLOPs 2537.56G 90.88

ConvNeXt

Model Params FLOPs Input Top1 Acc Download
ConvNeXtTiny 28M 4.49G 224 82.1 tiny_imagenet.h5
- ImageNet21k-ft1k 28M 4.49G 224 82.9 tiny_224_21k.h5
- ImageNet21k-ft1k 28M 13.19G 384 84.1 tiny_384_21k.h5
ConvNeXtSmall 50M 8.73G 224 83.1 small_imagenet.h5
- ImageNet21k-ft1k 50M 8.73G 224 84.6 small_224_21k.h5
- ImageNet21k-ft1k 50M 25.67G 384 85.8 small_384_21k.h5
ConvNeXtBase 89M 15.42G 224 83.8 base_224_imagenet.h5
ConvNeXtBase 89M 45.32G 384 85.1 base_384_imagenet.h5
- ImageNet21k-ft1k 89M 15.42G 224 85.8 base_224_21k.h5
- ImageNet21k-ft1k 89M 45.32G 384 86.8 base_384_21k.h5
ConvNeXtLarge 198M 34.46G 224 84.3 large_224_imagenet.h5
ConvNeXtLarge 198M 101.28G 384 85.5 large_384_imagenet.h5
- ImageNet21k-ft1k 198M 34.46G 224 86.6 large_224_21k.h5
- ImageNet21k-ft1k 198M 101.28G 384 87.5 large_384_21k.h5
ConvNeXtXLarge, 21k 350M 61.06G 224 87.0 xlarge_224_21k.h5
ConvNeXtXLarge, 21k 350M 179.43G 384 87.8 xlarge_384_21k.h5

CoTNet

Model Params FLOPs Input Top1 Acc Download
CotNet50 22.2M 3.25G 224 81.3 cotnet50_224_imagenet.h5
CotNeXt50 30.1M 4.3G 224 82.1
CotNetSE50D 23.1M 4.05G 224 81.6 cotnet_se50d_224_imagenet.h5
CotNet101 38.3M 6.07G 224 82.8 cotnet101_224_imagenet.h5
CotNeXt101 53.4M 8.2G 224 83.2
CotNetSE101D 40.9M 8.44G 224 83.2 cotnet_se101d_224_imagenet.h5
CotNetSE152D 55.8M 12.22G 224 84.0 cotnet_se152d_224_imagenet.h5
CotNetSE152D 55.8M 24.92G 320 84.6 cotnet_se152d_320_imagenet.h5

DaViT

Model Params FLOPs Input Top1 Acc Download
DaViT_T 28.36M 4.56G 224 82.8 davit_t_imagenet.h5
DaViT_S 49.75M 8.83G 224 84.2 davit_s_imagenet.h5
DaViT_B 87.95M 15.55G 224 84.6 davit_b_imagenet.h5
DaViT_L, 21k 196.8M 103.2G 384 87.5
DaViT_H, 1.5B 348.9M 327.3G 512 90.2
DaViT_G, 1.5B 1.406B 1.022T 512 90.4

EdgeNeXt

Model Params FLOPs Input Top1 Acc Download
EdgeNeXt_XX_Small 1.33M 266M 256 71.23 edgenext_xx_small_256_imagenet.h5
EdgeNeXt_X_Small 2.34M 547M 256 74.96 edgenext_x_small_256_imagenet.h5
EdgeNeXt_Small 5.59M 1.27G 256 79.41 edgenext_small_256_imagenet.h5
- usi 5.59M 1.27G 256 81.07 edgenext_small_256_usi.h5

EfficientFormer

Model Params FLOPs Input Top1 Acc Download
EfficientFormerL1, distill 12.3M 1.31G 224 79.2 l1_224_imagenet.h5
EfficientFormerL3, distill 31.4M 3.95G 224 82.4 l3_224_imagenet.h5
EfficientFormerL7, distill 74.4M 9.79G 224 83.3 l7_224_imagenet.h5

EfficientNet

V2 Model Params FLOPs Input Top1 Acc Download
EfficientNetV2B0 7.1M 0.72G 224 78.7 effv2b0-imagenet.h5
- ImageNet21k-ft1k 7.1M 0.72G 224 77.55? effv2b0-21k-ft1k.h5
EfficientNetV2B1 8.1M 1.21G 240 79.8 effv2b1-imagenet.h5
- ImageNet21k-ft1k 8.1M 1.21G 240 79.03? effv2b1-21k-ft1k.h5
EfficientNetV2B2 10.1M 1.71G 260 80.5 effv2b2-imagenet.h5
- ImageNet21k-ft1k 10.1M 1.71G 260 79.48? effv2b2-21k-ft1k.h5
EfficientNetV2B3 14.4M 3.03G 300 82.1 effv2b3-imagenet.h5
- ImageNet21k-ft1k 14.4M 3.03G 300 82.46? effv2b3-21k-ft1k.h5
EfficientNetV2T 13.6M 3.18G 288 82.34 effv2t-imagenet.h5
EfficientNetV2T_GC 13.7M 3.19G 288 82.46 effv2t-gc-imagenet.h5
EfficientNetV2S 21.5M 8.41G 384 83.9 effv2s-imagenet.h5
- ImageNet21k-ft1k 21.5M 8.41G 384 84.9 effv2s-21k-ft1k.h5
EfficientNetV2M 54.1M 24.69G 480 85.2 effv2m-imagenet.h5
- ImageNet21k-ft1k 54.1M 24.69G 480 86.2 effv2m-21k-ft1k.h5
EfficientNetV2L 119.5M 56.27G 480 85.7 effv2l-imagenet.h5
- ImageNet21k-ft1k 119.5M 56.27G 480 86.9 effv2l-21k-ft1k.h5
EfficientNetV2XL, 21k-ft1k 206.8M 93.66G 512 87.2 effv2xl-21k-ft1k.h5
V1 Model Params FLOPs Input Top1 Acc Download
EfficientNetV1B0 5.3M 0.39G 224 77.6 effv1-b0-imagenet.h5
- NoisyStudent 5.3M 0.39G 224 78.8 effv1-b0-noisy_student.h5
EfficientNetV1B1 7.8M 0.70G 240 79.6 effv1-b1-imagenet.h5
- NoisyStudent 7.8M 0.70G 240 81.5 effv1-b1-noisy_student.h5
EfficientNetV1B2 9.1M 1.01G 260 80.5 effv1-b2-imagenet.h5
- NoisyStudent 9.1M 1.01G 260 82.4 effv1-b2-noisy_student.h5
EfficientNetV1B3 12.2M 1.86G 300 81.9 effv1-b3-imagenet.h5
- NoisyStudent 12.2M 1.86G 300 84.1 effv1-b3-noisy_student.h5
EfficientNetV1B4 19.3M 4.46G 380 83.3 effv1-b4-imagenet.h5
- NoisyStudent 19.3M 4.46G 380 85.3 effv1-b4-noisy_student.h5
EfficientNetV1B5 30.4M 10.40G 456 84.3 effv1-b5-imagenet.h5
- NoisyStudent 30.4M 10.40G 456 86.1 effv1-b5-noisy_student.h5
EfficientNetV1B6 43.0M 19.29G 528 84.8 effv1-b6-imagenet.h5
- NoisyStudent 43.0M 19.29G 528 86.4 effv1-b6-noisy_student.h5
EfficientNetV1B7 66.3M 38.13G 600 85.2 effv1-b7-imagenet.h5
- NoisyStudent 66.3M 38.13G 600 86.9 effv1-b7-noisy_student.h5
EfficientNetV1L2, NoisyStudent 480.3M 477.98G 800 88.4 effv1-l2-noisy_student.h5

FBNetV3

Model Params FLOPs Input Top1 Acc Download
FBNetV3B 5.57M 539.82M 256 79.15 fbnetv3_b_imagenet.h5
FBNetV3D 10.31M 665.02M 256 79.68 fbnetv3_d_imagenet.h5
FBNetV3G 16.62M 1379.30M 256 82.05 fbnetv3_g_imagenet.h5

GCViT

Model Params FLOPs Input Top1 Acc Download
GCViT_XXTiny 12.0M 2.15G 224 79.8 gcvit_xx_tiny_224_imagenet.h5
GCViT_XTiny 20.0M 2.96G 224 82.04 gcvit_x_tiny_224_imagenet.h5
GCViT_Tiny 28.2M 4.83G 224 83.4 gcvit_tiny_224_imagenet.h5
GCViT_Small 51.1M 8.63G 224 83.95 gcvit_small_224_imagenet.h5
GCViT_Base 90.3M 14.9G 224 84.47 gcvit_base_224_imagenet.h5

GMLP

Model Params FLOPs Input Top1 Acc Download
GMLPTiny16 6M 1.35G 224 72.3
GMLPS16 20M 4.44G 224 79.6 gmlp_s16_imagenet.h5
GMLPB16 73M 15.82G 224 81.6

HaloNet

Model Params FLOPs Input Top1 Acc Download
HaloNetH0 5.5M 2.40G 256 77.9
HaloNetH1 8.1M 3.04G 256 79.9
HaloNetH2 9.4M 3.37G 256 80.4
HaloNetH3 11.8M 6.30G 320 81.9
HaloNetH4 19.1M 12.17G 384 83.3
- 21k 19.1M 12.17G 384 85.5
HaloNetH5 30.7M 32.61G 448 84.0
HaloNetH6 43.4M 53.20G 512 84.4
HaloNetH7 67.4M 119.64G 600 84.9
HaloNextECA26T 10.7M 2.43G 256 79.50 halonext_eca26t_256_imagenet.h5
HaloNet26T 12.5M 3.18G 256 79.13 halonet26t_256_imagenet.h5
HaloNetSE33T 13.7M 3.55G 256 80.99 halonet_se33t_256_imagenet.h5
HaloRegNetZB 11.68M 1.97G 224 81.042 haloregnetz_b_224_imagenet.h5
HaloNet50T 22.7M 5.29G 256 81.70 halonet50t_256_imagenet.h5
HaloBotNet50T 22.6M 5.02G 256 82.0 halobotnet50t_256_imagenet.h5

HorNet

Model Params FLOPs Input Top1 Acc Download
HorNetTiny 22.4M 4.01G 224 82.8 hornet_tiny_224_imagenet.h5
HorNetTinyGF 23.0M 3.94G 224 83.0 hornet_tiny_gf_224_imagenet.h5
HorNetSmall 49.5M 8.87G 224 83.8 hornet_small_224_imagenet.h5
HorNetSmallGF 50.4M 8.77G 224 84.0 hornet_small_gf_224_imagenet.h5
HorNetBase 87.3M 15.65G 224 84.2 hornet_base_224_imagenet.h5
HorNetBaseGF 88.4M 15.51G 224 84.3 hornet_base_gf_224_imagenet.h5
HorNetLarge 194.5M 34.91G 224 86.8 hornet_large_224_imagenet22k.h5
HorNetLargeGF 196.3M 34.72G 224 87.0 hornet_large_gf_224_imagenet22k.h5
HorNetLargeGF 201.8M 102.0G 384 87.7 hornet_large_gf_384_imagenet22k.h5

LCNet

Model Params FLOPs Input Top1 Acc Download
LCNet050 1.88M 46.02M 224 63.10 lcnet_050_imagenet.h5
LCNet075 2.36M 96.82M 224 68.82 lcnet_075_imagenet.h5
LCNet100 2.95M 158.28M 224 72.10 lcnet_100_imagenet.h5
LCNet150 4.52M 338.05M 224 73.71 lcnet_150_imagenet.h5
LCNet200 6.54M 585.35M 224 75.18 lcnet_200_imagenet.h5
LCNet250 9.04M 900.16M 224 76.60 lcnet_250_imagenet.h5

LeViT

Model Params FLOPs Input Top1 Acc Download
LeViT128S, distillation 7.8M 0.31G 224 76.6 levit128s_imagenet.h5
LeViT128, distillation 9.2M 0.41G 224 78.6 levit128_imagenet.h5
LeViT192, distillation 11M 0.66G 224 80.0 levit192_imagenet.h5
LeViT256, distillation 19M 1.13G 224 81.6 levit256_imagenet.h5
LeViT384, distillation 39M 2.36G 224 82.6 levit384_imagenet.h5

MaxViT

Model Params FLOPs Input Top1 Acc Download
MaxViT_Tiny 31M 5.6G 224 83.62 maxvit_t_224_imagenet.h5
MaxViT_Tiny 31M 17.7G 384 85.24
MaxViT_Tiny 31M 33.7G 512 85.72
MaxViT_Small 69M 11.7G 224 84.45 maxvit_t_224_imagenet.h5
MaxViT_Small 69M 36.1G 384 85.74
MaxViT_Small 69M 67.6G 512 86.19
MaxViT_Base 119M 24.2G 224 84.95 maxvit_t_224_imagenet.h5
MaxViT_Base 119M 74.2G 384 86.34
- imagenet21k-ft1k 119M 74.2G 384 88.24
MaxViT_Base 119M 138.5G 512 86.66
- imagenet21k-ft1k 119M 138.5G 512 88.38
MaxViT_Large 212M 43.9G 224 85.17 maxvit_t_224_imagenet.h5
MaxViT_Large 212M 133.1G 384 86.40
- imagenet21k-ft1k 212M 133.1G 384 88.32
MaxViT_Large 212M 245.4G 512 86.70
- imagenet21k-ft1k 212M 245.4G 512 88.46
MaxViT_XLarge, imagenet21k-ft1k 475M 293.7G 384 88.51
MaxViT_XLarge, imagenet21k-ft1k 475M 535.2G 512 88.70

MLP mixer

Model Params FLOPs Input Top1 Acc Download
MLPMixerS32, JFT 19.1M 1.01G 224 68.70
MLPMixerS16, JFT 18.5M 3.79G 224 73.83
MLPMixerB32, JFT 60.3M 3.25G 224 75.53
- imagenet_sam 60.3M 3.25G 224 72.47 b32_imagenet_sam.h5
MLPMixerB16 59.9M 12.64G 224 76.44 b16_imagenet.h5
- imagenet21k 59.9M 12.64G 224 80.64 b16_imagenet21k.h5
- imagenet_sam 59.9M 12.64G 224 77.36 b16_imagenet_sam.h5
- JFT 59.9M 12.64G 224 80.00
MLPMixerL32, JFT 206.9M 11.30G 224 80.67
MLPMixerL16 208.2M 44.66G 224 71.76 l16_imagenet.h5
- imagenet21k 208.2M 44.66G 224 82.89 l16_imagenet21k.h5
- input 448 208.2M 178.54G 448 83.91
- input 224, JFT 208.2M 44.66G 224 84.82
- input 448, JFT 208.2M 178.54G 448 86.78
MLPMixerH14, JFT 432.3M 121.22G 224 86.32
- input 448, JFT 432.3M 484.73G 448 87.94

MobileNetV3

Model Params FLOPs Input Top1 Acc Download
MobileNetV3Small050 1.29M 24.92M 224 57.89 small_050_imagenet.h5
MobileNetV3Small075 2.04M 44.35M 224 65.24 small_075_imagenet.h5
MobileNetV3Small100 2.54M 57.62M 224 67.66 small_100_imagenet.h5
MobileNetV3Large075 3.99M 156.30M 224 73.44 large_075_imagenet.h5
MobileNetV3Large100 5.48M 218.73M 224 75.77 large_100_imagenet.h5
- miil 5.48M 218.73M 224 77.92 large_100_miil.h5

MobileViT

Model Params FLOPs Input Top1 Acc Download
MobileViT_XXS 1.3M 0.42G 256 69.0 mobilevit_xxs_imagenet
MobileViT_XS 2.3M 1.05G 256 74.7 mobilevit_xs_imagenet
MobileViT_S 5.6M 2.03G 256 78.3 mobilevit_s_imagenet

MobileViT_V2

Model Params FLOPs Input Top1 Acc Download
MobileViT_V2_050 1.37M 0.47G 256 70.18 v2_050_256_imagenet.h5
MobileViT_V2_075 2.87M 1.04G 256 75.56 v2_075_256_imagenet.h5
MobileViT_V2_100 4.90M 1.83G 256 78.09 v2_100_256_imagenet.h5
MobileViT_V2_125 7.48M 2.84G 256 79.65 v2_125_256_imagenet.h5
MobileViT_V2_150 10.6M 4.07G 256 80.38 v2_150_256_imagenet.h5
- imagenet22k 10.6M 4.07G 256 81.46 v2_150_256_imagenet22k.h5
- imagenet22k, 384 10.6M 9.15G 384 82.60 v2_150_384_imagenet22k.h5
MobileViT_V2_175 14.3M 5.52G 256 80.84 v2_175_256_imagenet.h5
- imagenet22k 14.3M 5.52G 256 81.94 v2_175_256_imagenet22k.h5
- imagenet22k, 384 14.3M 12.4G 384 82.93 v2_175_384_imagenet22k.h5
MobileViT_V2_200 18.4M 7.12G 256 81.17 v2_200_256_imagenet.h5
- imagenet22k 18.4M 7.12G 256 82.36 v2_200_256_imagenet22k.h5
- imagenet22k, 384 18.4M 16.2G 384 83.41 v2_200_384_imagenet22k.h5

NAT

Model Params FLOPs Input Top1 Acc Download
NAT_Mini 20.0M 2.73G 224 81.8 nat_mini_imagenet.h5
NAT_Tiny 27.9M 4.34G 224 83.2 nat_tiny_imagenet.h5
NAT_Small 50.7M 7.84G 224 83.7 nat_small_imagenet.h5
NAT_Base 89.8M 13.76G 224 84.3 nat_base_imagenet.h5

NFNets

Model Params FLOPs Input Top1 Acc Download
NFNetL0 35.07M 7.13G 288 82.75 nfnetl0_imagenet.h5
NFNetF0 71.5M 12.58G 256 83.6 nfnetf0_imagenet.h5
NFNetF1 132.6M 35.95G 320 84.7 nfnetf1_imagenet.h5
NFNetF2 193.8M 63.24G 352 85.1 nfnetf2_imagenet.h5
NFNetF3 254.9M 115.75G 416 85.7 nfnetf3_imagenet.h5
NFNetF4 316.1M 216.78G 512 85.9 nfnetf4_imagenet.h5
NFNetF5 377.2M 291.73G 544 86.0 nfnetf5_imagenet.h5
NFNetF6 SAM 438.4M 379.75G 576 86.5 nfnetf6_imagenet.h5
NFNetF7 499.5M 481.80G 608
ECA_NFNetL0 24.14M 7.12G 288 82.58 eca_nfnetl0_imagenet.h5
ECA_NFNetL1 41.41M 14.93G 320 84.01 eca_nfnetl1_imagenet.h5
ECA_NFNetL2 56.72M 30.12G 384 84.70 eca_nfnetl2_imagenet.h5
ECA_NFNetL3 72.04M 52.73G 448

RegNetY

Model Params FLOPs Input Top1 Acc Download
RegNetY040 20.65M 3.98G 224 82.3 regnety_040_imagenet.h5
RegNetY064 30.58M 6.36G 224 83.0 regnety_064_imagenet.h5
RegNetY080 39.18M 7.97G 224 83.17 regnety_080_imagenet.h5
RegNetY160 83.59M 15.92G 224 82.0 regnety_160_imagenet.h5
RegNetY320 145.05M 32.29G 224 82.5 regnety_320_imagenet.h5

RegNetZ

Model Params FLOPs Input Top1 Acc Download
RegNetZB16 9.72M 1.44G 224 79.868 regnetz_b16_imagenet.h5
RegNetZC16 13.46M 2.50G 256 82.164 regnetz_c16_imagenet.h5
RegNetZC16_EVO 13.49M 2.55G 256 81.9 regnetz_c16_evo_imagenet.h5
RegNetZD32 27.58M 5.96G 256 83.422 regnetz_d32_imagenet.h5
RegNetZD8 23.37M 3.95G 256 83.5 regnetz_d8_imagenet.h5
RegNetZD8_EVO 23.46M 4.61G 256 83.42 regnetz_d8_evo_imagenet.h5
RegNetZE8 57.70M 9.88G 256 84.5 regnetz_e8_imagenet.h5

ResMLP

Model Params FLOPs Input Top1 Acc Download
ResMLP12 15M 3.02G 224 77.8 resmlp12_imagenet.h5
ResMLP24 30M 5.98G 224 80.8 resmlp24_imagenet.h5
ResMLP36 116M 8.94G 224 81.1 resmlp36_imagenet.h5
ResMLP_B24 129M 100.39G 224 83.6 resmlp_b24_imagenet.h5
- imagenet22k 129M 100.39G 224 84.4 resmlp_b24_imagenet22k.h5

ResNeSt

Model Params FLOPs Input Top1 Acc Download
resnest50 28M 5.38G 224 81.03 resnest50.h5
resnest101 49M 13.33G 256 82.83 resnest101.h5
resnest200 71M 35.55G 320 83.84 resnest200.h5
resnest269 111M 77.42G 416 84.54 resnest269.h5

ResNetD

Model Params FLOPs Input Top1 Acc Download
ResNet50D 25.58M 4.33G 224 80.530 resnet50d.h5
ResNet101D 44.57M 8.04G 224 83.022 resnet101d.h5
ResNet152D 60.21M 11.75G 224 83.680 resnet152d.h5
ResNet200D 64.69M 15.25G 224 83.962 resnet200d.h5

ResNetQ

Model Params FLOPs Input Top1 Acc Download
ResNet51Q 35.7M 4.87G 224 82.36 resnet51q.h5
ResNet61Q 36.8M 5.96G 224

ResNeXt

Model Params FLOPs Input Top1 Acc Download
ResNeXt50 (32x4d) 25M 4.23G 224 79.768 resnext50_imagenet.h5
- SWSL 25M 4.23G 224 82.182 resnext50_swsl.h5
ResNeXt50D (32x4d + deep) 25M 4.47G 224 79.676 resnext50d_imagenet.h5
ResNeXt101 (32x4d) 42M 7.97G 224 80.334 resnext101_imagenet.h5
- SWSL 42M 7.97G 224 83.230 resnext101_swsl.h5
ResNeXt101W (32x8d) 89M 16.41G 224 79.308 resnext101_imagenet.h5
- SWSL 89M 16.41G 224 84.284 resnext101w_swsl.h5
ResNeXt101W_64 (64x4d) 83.46M 15.46G 224 82.46 resnext101w_64_imagenet.h5

SwinTransformerV2

Model Params FLOPs Input Top1 Acc Download
SwinTransformerV2Tiny_ns 28.3M 4.69G 224 81.8 tiny_ns_224_imagenet.h5
SwinTransformerV2Small_ns 49.7M 9.12G 224 83.5 small_ns_224_imagenet.h5
SwinTransformerV2Tiny_window8 28.3M 5.99G 256 81.8 tiny_window8_256.h5
SwinTransformerV2Tiny_window16 28.3M 6.75G 256 82.8 tiny_window16_256.h5
SwinTransformerV2Small_window8 49.7M 11.63G 256 83.7 small_window8_256.h5
SwinTransformerV2Small_window16 49.7M 12.93G 256 84.1 small_window16_256.h5
SwinTransformerV2Base_window8 87.9M 20.44G 256 84.2 base_window8_256.h5
SwinTransformerV2Base_window16 87.9M 22.17G 256 84.6 base_window16_256.h5
SwinTransformerV2Base_window16, 22k 87.9M 22.17G 256 86.2 base_window16_256_22k.h5
SwinTransformerV2Base_window24, 22k 87.9M 55.89G 384 87.1 base_window24_384_22k.h5
SwinTransformerV2Large_window16, 22k 196.7M 48.03G 256 86.9 large_window16_256_22k.h5
SwinTransformerV2Large_window24, 22k 196.7M 117.1G 384 87.6 large_window24_384_22k.h5

TinyNet

Model Params FLOPs Input Top1 Acc Download
TinyNetE 2.04M 25.22M 106 59.86 tinynet_e_imagenet.h5
TinyNetD 2.34M 53.35M 152 66.96 tinynet_d_imagenet.h5
TinyNetC 2.46M 103.22M 184 71.23 tinynet_c_imagenet.h5
TinyNetB 3.73M 206.28M 188 74.98 tinynet_b_imagenet.h5
TinyNetA 6.19M 343.74M 192 77.65 tinynet_a_imagenet.h5

UniFormer

Model Params FLOPs Input Top1 Acc Download
UniformerSmall32 + TL 22M 3.66G 224 83.4 small_32_224_token_label
UniformerSmall64 22M 3.66G 224 82.9 small_64_imagenet
- Token Labeling 22M 3.66G 224 83.4 small_64_token_label
UniformerSmallPlus32 24M 4.24G 224 83.4 small_plus_32_imagenet
- Token Labeling 24M 4.24G 224 83.9 small_plus_32_token_label
UniformerSmallPlus64 24M 4.23G 224 83.4 small_plus_64_imagenet
- Token Labeling 24M 4.23G 224 83.6 small_plus_64_token_label
UniformerBase32 + TL 50M 8.32G 224 85.1 base_32_224_token_label
UniformerBase64 50M 8.31G 224 83.8 base_64_imagenet
- Token Labeling 50M 8.31G 224 84.8 base_64_224_token_label
UniformerLarge64 + TL 100M 19.79G 224 85.6 large_64_224_token_label
UniformerLarge64 + TL 100M 63.11G 384 86.3 large_64_384_token_label

VOLO

Model Params FLOPs Input Top1 Acc Download
VOLO_d1 27M 4.82G 224 84.2 volo_d1_224_imagenet.h5
- 384 27M 14.22G 384 85.2 volo_d1_384_imagenet.h5
VOLO_d2 59M 9.78G 224 85.2 volo_d2_224_imagenet.h5
- 384 59M 28.84G 384 86.0 volo_d2_384_imagenet.h5
VOLO_d3 86M 13.80G 224 85.4 volo_d3_224_imagenet.h5
- 448 86M 55.50G 448 86.3 volo_d3_448_imagenet.h5
VOLO_d4 193M 29.39G 224 85.7 volo_d4_224_imagenet.h5
- 448 193M 117.81G 448 86.8 volo_d4_448_imagenet.h5
VOLO_d5 296M 53.34G 224 86.1 volo_d5_224_imagenet.h5
- 448 296M 213.72G 448 87.0 volo_d5_448_imagenet.h5
- 512 296M 279.36G 512 87.1 volo_d5_512_imagenet.h5

WaveMLP

Model Params FLOPs Input Top1 Acc Download
WaveMLP_T 17M 2.47G 224 80.9 wavemlp_t_imagenet.h5
WaveMLP_S 30M 4.55G 224 82.9 wavemlp_s_imagenet.h5
WaveMLP_M 44M 7.92G 224 83.3 wavemlp_m_imagenet.h5
WaveMLP_B 63M 10.26G 224 83.6

Detection Models

EfficientDet

Model Params FLOPs Input COCO val AP test AP Download
EfficientDetD0 3.9M 2.55G 512 34.3 34.6 efficientdet_d0.h5
- Det-AdvProp 3.9M 2.55G 512 35.1 35.3
EfficientDetD1 6.6M 6.13G 640 40.2 40.5 efficientdet_d1.h5
- Det-AdvProp 6.6M 6.13G 640 40.8 40.9
EfficientDetD2 8.1M 11.03G 768 43.5 43.9 efficientdet_d2.h5
- Det-AdvProp 8.1M 11.03G 768 44.3 44.3
EfficientDetD3 12.0M 24.95G 896 46.8 47.2 efficientdet_d3.h5
- Det-AdvProp 12.0M 24.95G 896 47.7 48.0
EfficientDetD4 20.7M 55.29G 1024 49.3 49.7 efficientdet_d4.h5
- Det-AdvProp 20.7M 55.29G 1024 50.4 50.4
EfficientDetD5 33.7M 135.62G 1280 51.2 51.5 efficientdet_d5.h5
- Det-AdvProp 33.7M 135.62G 1280 52.2 52.5
EfficientDetD6 51.9M 225.93G 1280 52.1 52.6 efficientdet_d6.h5
EfficientDetD7 51.9M 325.34G 1536 53.4 53.7 efficientdet_d7.h5
EfficientDetD7X 77.0M 410.87G 1536 54.4 55.1 efficientdet_d7x.h5
EfficientDetLite0 3.2M 0.98G 320 27.5 26.41 efficientdet_lite0.h5
EfficientDetLite1 4.2M 1.97G 384 32.6 31.50 efficientdet_lite1.h5
EfficientDetLite2 5.3M 3.38G 448 36.2 35.06 efficientdet_lite2.h5
EfficientDetLite3 8.4M 7.50G 512 39.9 38.77 efficientdet_lite3.h5
EfficientDetLite3X 9.3M 14.01G 640 44.0 42.64 efficientdet_lite3x.h5
EfficientDetLite4 15.1M 20.20G 640 44.4 43.18 efficientdet_lite4.h5

YOLOR

Model Params FLOPs Input COCO val AP test AP Download
YOLOR_CSP 52.9M 60.25G 640 50.0 52.8 yolor_csp_coco.h5
YOLOR_CSPX 99.8M 111.11G 640 51.5 54.8 yolor_csp_x_coco.h5
YOLOR_P6 37.3M 162.87G 1280 52.5 55.7 yolor_p6_coco.h5
YOLOR_W6 79.9M 226.67G 1280 53.6 ? 56.9 yolor_w6_coco.h5
YOLOR_E6 115.9M 341.62G 1280 50.3 ? 57.6 yolor_e6_coco.h5
YOLOR_D6 151.8M 467.88G 1280 50.8 ? 58.2 yolor_d6_coco.h5

YOLOX

Model Params FLOPs Input COCO val AP test AP Download
YOLOXNano 0.91M 0.53G 416 25.8 yolox_nano_coco.h5
YOLOXTiny 5.06M 3.22G 416 32.8 yolox_tiny_coco.h5
YOLOXS 9.0M 13.39G 640 40.5 40.5 yolox_s_coco.h5
YOLOXM 25.3M 36.84G 640 46.9 47.2 yolox_m_coco.h5
YOLOXL 54.2M 77.76G 640 49.7 50.1 yolox_l_coco.h5
YOLOXX 99.1M 140.87G 640 51.5 51.5 yolox_x_coco.h5

Other implemented tensorflow or keras models

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}}
}
  • Latest DOI: DOI

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License: Apache Software License (Apache 2.0)

Author: Leondgarse

Tags tensorflow, keras, cv, attention, pretrained, models, kecam

Requires: Python >=3.6

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