- coco_train_script.py is under testing
- Currently recommended TF version is
tensorflow==2.8.0
. 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:
Refer to each sub directory for detail usage.
pip install -U keras-cv-attention-models # Or pip install -U git+https://github.com/leondgarse/keras_cv_attention_models
- Basic model prediction
Or just use model preset
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)]
preprocess_input
anddecode_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), ...]
- Exclude model top layers by set
num_classes=0
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 differentinput_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 ofkeras_cv_attention_models
. It's__init__.py
only with one linefrom 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
- attention_layers is
__init__.py
only, which imports core layers defined in model architectures. LikeRelativePositionalEmbedding
frombotnet
,outlook_attention
fromvolo
.
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 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 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 ajson
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 typicalResNet50
architecture withConv2D use_bias=False
andpadding
likePyTorch
.- Default parameters for
train_script.py
is likeA3
configuration from ResNet strikes back: An improved training procedure in timm withbatch_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
- 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
eval_script.py
is used for evaluating 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
-
Currently still under testing.
-
COCO contains more detail usage.
-
custom_dataset_script.py
can be used creating ajson
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
isEfficientDetD0
withinput_shape=(256, 256, 3), batch_size=64, mosaic_mix_prob=0.5, freeze_backbone_epochs=32, total_epochs=105
. Technically, it's anypyramid structure backbone
+EfficientDet / YOLOX header / YOLOR header
+anchor_free / yolor_anchors / efficientdet_anchors
combination supported. -
Currently 3 types anchors supported,
- use_anchor_free_mode controls if using typical
YOLOX anchor_free mode
strategy. - use_yolor_anchors_mode controls if using yolor anchors.
- Default is
use_anchor_free_mode=False, use_yolor_anchors_mode=False
, means usingefficientdet
preset anchors.
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_anchors 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_mode CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --backbone resnest.ResNest50 --use_anchor_free_mode # ConvNeXtTiny backbone + EfficientDetD0 header using yolor anchors CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --backbone uniformer.UniformerSmall32 --use_yolor_anchors_mode # Typical YOLOXS with anchor_free_mode CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --det_header yolox.YOLOXS --use_anchor_free_mode # YOLOXS with efficientdet anchors CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --det_header yolox.YOLOXS # ConvNeXtTiny backbone + YOLOX header with yolor anchors CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --backbone coatnet.CoAtNet0 --det_header yolox.YOLOX --use_yolor_anchors_mode # Typical YOLOR_P6 with yolor anchors CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --det_header yolor.YOLOR_P6 --use_yolor_anchors_mode # YOLOR_P6 with anchor_free_mode CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --det_header yolor.YOLOR_P6 --use_anchor_free_mode # ConvNeXtTiny backbone + YOLOR header with efficientdet anchors CUDA_VISIBLE_DEVICES='0' ./coco_train_script.py --backbone convnext.ConvNeXtTiny --det_header yolor.YOLOR
Note: COCO training still under testing, may change parameters and default behaviors. Take the risk if would like help developing.
- use_anchor_free_mode controls if using typical
-
coco_eval_script.py
is used for evaluating model AP / AR on COCO validation set. It has a dependencypip install pycocotools
which is not in package requirements. More usage can be found in COCO Evaluation.# resize method for EfficientDetD0 is bilinear w/o antialias CUDA_VISIBLE_DEVICES='1' ./coco_eval_script.py -m efficientdet.EfficientDetD0 --resize_method bilinear --disable_antialias # Specify --use_anchor_free_mode for YOLOX, and BGR input format CUDA_VISIBLE_DEVICES='1' ./coco_eval_script.py -m yolox.YOLOXTiny --use_anchor_free_mode --use_bgr_input --nms_method hard --nms_iou_or_sigma 0.65 # Specify --use_yolor_anchors_mode for YOLOR. CUDA_VISIBLE_DEVICES='1' ./coco_eval_script.py -m yolor.YOLOR_CSP --use_yolor_anchors_mode --nms_method hard --nms_iou_or_sigma 0.65 \ --nms_max_output_size 300 --nms_topk -1 --letterbox_pad 64 --input_shape 704 # Specific h5 model CUDA_VISIBLE_DEVICES='1' ./coco_eval_script.py -m checkpoints/yoloxtiny_yolor_anchor.h5 --use_yolor_anchors_mode
- 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)
- Currently
TFLite
not supportingConv2D with groups>1
/gelu
/tf.image.extract_patches
/tf.transpose with len(perm) > 4
. Some operations could be supported intf-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 withactivation="gelu/approximate"
oractivation="gelu/app"
will setapproximate=True
forgelu
. 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 toSplitConv
usingsplit -> 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 togelu approximate=True
, andtf.image.extract_patches
to aConv2D
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 longertf.transpose
perm
.
- Keras AotNet is just a
ResNet
/ResNetV2
like framework, that set parameters likeattn_types
andse_ratio
and others, which is used to apply different types attention layer. Works likebyoanet
/byobnet
fromtimm
. - Default parameters set is a typical
ResNet
architecture withConv2D use_bias=False
andpadding
likePyTorch
.
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()
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
BeitBasePatch16, 21k | 86.53M | 224 | 85.240 | beit_base_patch16_224.h5 |
86.74M | 384 | 86.808 | beit_base_patch16_384.h5 | |
BeitLargePatch16, 21k | 304.43M | 224 | 87.476 | beit_large_patch16_224.h5 |
305.00M | 384 | 88.382 | beit_large_patch16_384.h5 | |
305.67M | 512 | 88.584 | beit_large_patch16_512.h5 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
BotNet50 | 21M | 224 | ||
BotNet101 | 41M | 224 | ||
BotNet152 | 56M | 224 | ||
BotNet26T | 12.5M | 256 | 79.246 | botnet26t_256_imagenet.h5 |
BotNextECA26T | 10.59M | 256 | 79.270 | botnext_eca26t_256_imagenet.h5 |
BotNetSE33T | 13.7M | 256 | 81.2 | botnet_se33t_256_imagenet.h5 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
CMTTiny, (Self trained 105 epochs) | 9.5M | 160 | 77.4 | |
- 305 epochs | 9.5M | 160 | 78.8 | cmt_tiny_160_imagenet |
- evaluate 224 (not fine-tuned) | 9.5M | 224 | 80.1 | |
CMTTiny, 1000 epochs | 9.5M | 160 | 79.2 | |
CMTXS | 15.2M | 192 | 81.8 | |
CMTSmall | 25.1M | 224 | 83.5 | |
CMTBig | 45.7M | 256 | 84.5 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
CoaTLiteTiny | 5.7M | 224 | 77.5 | coat_lite_tiny_imagenet.h5 |
CoaTLiteMini | 11M | 224 | 79.1 | coat_lite_mini_imagenet.h5 |
CoaTLiteSmall | 20M | 224 | 81.9 | coat_lite_small_imagenet.h5 |
CoaTTiny | 5.5M | 224 | 78.3 | coat_tiny_imagenet.h5 |
CoaTMini | 10M | 224 | 81.0 | coat_mini_imagenet.h5 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
CoAtNet0 (Self trained 105 epochs) | 23.8M | 160 | 80.50 | coatnet0_160_imagenet.h5 |
- fine-tune 224, 37 epochs | 23.8M | 224 | 82.23 | coatnet0_224_imagenet.h5 |
CoAtNet0 | 25M | 224 | 81.6 | |
CoAtNet0, Strided DConv | 25M | 224 | 82.0 | |
CoAtNet1 | 42M | 224 | 83.3 | |
CoAtNet1, Strided DConv | 42M | 224 | 83.5 | |
CoAtNet2 | 75M | 224 | 84.1 | |
CoAtNet2, Strided DConv | 75M | 224 | 84.1 | |
CoAtNet2, ImageNet-21k pretrain | 75M | 224 | 87.1 | |
CoAtNet3 | 168M | 224 | 84.5 | |
CoAtNet3, ImageNet-21k pretrain | 168M | 224 | 87.6 | |
CoAtNet3, ImageNet-21k pretrain | 168M | 512 | 87.9 | |
CoAtNet4, ImageNet-21k pretrain | 275M | 512 | 88.1 | |
CoAtNet4, ImageNet-21K + PT-RA-E150 | 275M | 512 | 88.56 |
JFT pre-trained models accuracy
Model | Image resolution | Reported Params | self-defined Params | Top1 Acc |
---|---|---|---|---|
CoAtNet3 | 384 | 168M | 162.96M | 88.52 |
CoAtNet3 | 512 | 168M | 163.57M | 88.81 |
CoAtNet4 | 512 | 275M | 273.10M | 89.11 |
CoAtNet5 | 512 | 688M | 680.47M | 89.77 |
CoAtNet6 | 512 | 1.47B | 1.340B | 90.45 |
CoAtNet7 | 512 | 2.44B | 2.422B | 90.88 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
ConvNeXtTiny | 28M | 224 | 82.1 | tiny_imagenet.h5 |
ConvNeXtSmall | 50M | 224 | 83.1 | small_imagenet.h5 |
ConvNeXtBase | 89M | 224 | 83.8 | base_224_imagenet.h5 |
ConvNeXtBase | 89M | 384 | 85.1 | base_384_imagenet.h5 |
- ImageNet21k-ft1k | 89M | 224 | 85.8 | base_224_21k.h5 |
- ImageNet21k-ft1k | 89M | 384 | 86.8 | base_384_21k.h5 |
ConvNeXtLarge | 198M | 224 | 84.3 | large_224_imagenet.h5 |
ConvNeXtLarge | 198M | 384 | 85.5 | large_384_imagenet.h5 |
- ImageNet21k-ft1k | 198M | 224 | 86.6 | large_224_21k.h5 |
- ImageNet21k-ft1k | 198M | 384 | 87.5 | large_384_21k.h5 |
ConvNeXtXLarge, 21k | 350M | 224 | 87.0 | xlarge_224_21k.h5 |
ConvNeXtXLarge, 21k | 350M | 384 | 87.8 | xlarge_384_21k.h5 |
Model | Params | Image resolution | FLOPs | Top1 Acc | Download |
---|---|---|---|---|---|
CotNet50 | 22.2M | 224 | 3.3 | 81.3 | cotnet50_224_imagenet.h5 |
CoTNeXt50 | 30.1M | 224 | 4.3 | 82.1 | |
CotNetSE50D | 23.1M | 224 | 4.1 | 81.6 | cotnet_se50d_224_imagenet.h5 |
CotNet101 | 38.3M | 224 | 6.1 | 82.8 | cotnet101_224_imagenet.h5 |
CoTNeXt-101 | 53.4M | 224 | 8.2 | 83.2 | |
CotNetSE101D | 40.9M | 224 | 8.5 | 83.2 | cotnet_se101d_224_imagenet.h5 |
CotNetSE152D | 55.8M | 224 | 17.0 | 84.0 | cotnet_se152d_224_imagenet.h5 |
CotNetSE152D | 55.8M | 320 | 26.5 | 84.6 | cotnet_se152d_320_imagenet.h5 |
- Keras EfficientNet includes implementation of PDF 2104.00298 EfficientNetV2: Smaller Models and Faster Training.
V2 Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
EfficientNetV2B0 | 7.1M | 224 | 78.7 | effv2b0-imagenet.h5 |
- ImageNet21k-ft1k | 7.1M | 224 | 77.55? | effv2b0-21k-ft1k.h5 |
EfficientNetV2B1 | 8.1M | 240 | 79.8 | effv2b1-imagenet.h5 |
- ImageNet21k-ft1k | 8.1M | 240 | 79.03? | effv2b1-21k-ft1k.h5 |
EfficientNetV2B2 | 10.1M | 260 | 80.5 | effv2b2-imagenet.h5 |
- ImageNet21k-ft1k | 10.1M | 260 | 79.48? | effv2b2-21k-ft1k.h5 |
EfficientNetV2B3 | 14.4M | 300 | 82.1 | effv2b3-imagenet.h5 |
- ImageNet21k-ft1k | 14.4M | 300 | 82.46? | effv2b3-21k-ft1k.h5 |
EfficientNetV2T | 13.6M | 320 | 82.5 | effv2t-imagenet.h5 |
EfficientNetV2S | 21.5M | 384 | 83.9 | effv2s-imagenet.h5 |
- ImageNet21k-ft1k | 21.5M | 384 | 84.9 | effv2s-21k-ft1k.h5 |
EfficientNetV2M | 54.1M | 480 | 85.2 | effv2m-imagenet.h5 |
- ImageNet21k-ft1k | 54.1M | 480 | 86.2 | effv2m-21k-ft1k.h5 |
EfficientNetV2L | 119.5M | 480 | 85.7 | effv2l-imagenet.h5 |
- ImageNet21k-ft1k | 119.5M | 480 | 86.9 | effv2l-21k-ft1k.h5 |
EfficientNetV2XL, 21k-ft1k | 206.8M | 512 | 87.2 | effv2xl-21k-ft1k.h5 |
V1 Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
EfficientNetV1B0 | 5.3M | 224 | 77.6 | effv1-b0-imagenet.h5 |
- NoisyStudent | 5.3M | 224 | 78.8 | effv1-b0-noisy_student.h5 |
EfficientNetV1B1 | 7.8M | 240 | 79.6 | effv1-b1-imagenet.h5 |
- NoisyStudent | 7.8M | 240 | 81.5 | effv1-b1-noisy_student.h5 |
EfficientNetV1B2 | 9.1M | 260 | 80.5 | effv1-b2-imagenet.h5 |
- NoisyStudent | 9.1M | 260 | 82.4 | effv1-b2-noisy_student.h5 |
EfficientNetV1B3 | 12.2M | 300 | 81.9 | effv1-b3-imagenet.h5 |
- NoisyStudent | 12.2M | 300 | 84.1 | effv1-b3-noisy_student.h5 |
EfficientNetV1B4 | 19.3M | 380 | 83.3 | effv1-b4-imagenet.h5 |
- NoisyStudent | 19.3M | 380 | 85.3 | effv1-b4-noisy_student.h5 |
EfficientNetV1B5 | 30.4M | 456 | 84.3 | effv1-b5-imagenet.h5 |
- NoisyStudent | 30.4M | 456 | 86.1 | effv1-b5-noisy_student.h5 |
EfficientNetV1B6 | 43.0M | 528 | 84.8 | effv1-b6-imagenet.h5 |
- NoisyStudent | 43.0M | 528 | 86.4 | effv1-b6-noisy_student.h5 |
EfficientNetV1B7 | 66.3M | 600 | 85.2 | effv1-b7-imagenet.h5 |
- NoisyStudent | 66.3M | 600 | 86.9 | effv1-b7-noisy_student.h5 |
EfficientNetV1L2, NoisyStudent | 480.3M | 800 | 88.4 | effv1-l2-noisy_student.h5 |
- Keras FBNetV3 includes implementation of PDF 2006.02049 FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
FBNetV3B | 5.57M | 256 | 79.15 | fbnetv3_b_imagenet.h5 |
FBNetV3D | 10.31M | 256 | 79.68 | fbnetv3_d_imagenet.h5 |
FBNetV3G | 16.62M | 256 | 82.05 | fbnetv3_g_imagenet.h5 |
- Keras GMLP includes implementation of PDF 2105.08050 Pay Attention to MLPs.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
GMLPTiny16 | 6M | 224 | 72.3 | |
GMLPS16 | 20M | 224 | 79.6 | gmlp_s16_imagenet.h5 |
GMLPB16 | 73M | 224 | 81.6 |
- Keras HaloNet is for PDF 2103.12731 Scaling Local Self-Attention for Parameter Efficient Visual Backbones.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
HaloNetH0 | 5.5M | 256 | 77.9 | |
HaloNetH1 | 8.1M | 256 | 79.9 | |
HaloNetH2 | 9.4M | 256 | 80.4 | |
HaloNetH3 | 11.8M | 320 | 81.9 | |
HaloNetH4 | 19.1M | 384 | 83.3 | |
- 21k | 19.1M | 384 | 85.5 | |
HaloNetH5 | 30.7M | 448 | 84.0 | |
HaloNetH6 | 43.4M | 512 | 84.4 | |
HaloNetH7 | 67.4M | 600 | 84.9 | |
HaloNextECA26T | 10.7M | 256 | 79.50 | halonext_eca26t_256_imagenet.h5 |
HaloNet26T | 12.5M | 256 | 79.13 | halonet26t_256_imagenet.h5 |
HaloNetSE33T | 13.7M | 256 | 80.99 | halonet_se33t_256_imagenet.h5 |
HaloRegNetZB | 11.68M | 224 | 81.042 | haloregnetz_b_224_imagenet.h5 |
HaloNet50T | 22.7M | 256 | 81.70 | halonet50t_256_imagenet.h5 |
HaloBotNet50T | 22.6M | 256 | 82.0 | halobotnet50t_256_imagenet.h5 |
- Keras LCNet includes implementation of PDF 2109.15099 PP-LCNet: A Lightweight CPU Convolutional Neural Network.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
LCNet050 | 1.88M | 224 | 63.10 | lcnet_050_imagenet.h5 |
LCNet075 | 2.36M | 224 | 68.82 | lcnet_075_imagenet.h5 |
LCNet100 | 2.95M | 224 | 72.10 | lcnet_100_imagenet.h5 |
LCNet150 | 4.52M | 224 | 73.71 | lcnet_150_imagenet.h5 |
LCNet200 | 6.54M | 224 | 75.18 | lcnet_200_imagenet.h5 |
LCNet250 | 9.04M | 224 | 76.60 | lcnet_250_imagenet.h5 |
- Keras LeViT is for PDF 2104.01136 LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
LeViT128S, distillation | 7.8M | 224 | 76.6 | levit128s_imagenet.h5 |
LeViT128, distillation | 9.2M | 224 | 78.6 | levit128_imagenet.h5 |
LeViT192, distillation | 11M | 224 | 80.0 | levit192_imagenet.h5 |
LeViT256, distillation | 19M | 224 | 81.6 | levit256_imagenet.h5 |
LeViT384, distillation | 39M | 224 | 82.6 | levit384_imagenet.h5 |
- Keras MLP mixer includes implementation of PDF 2105.01601 MLP-Mixer: An all-MLP Architecture for Vision.
- Models
Top1 Acc
isPre-trained on JFT-300M
model accuray onImageNet 1K
from paper.
Model | Params | Top1 Acc | ImageNet | Imagenet21k | ImageNet SAM |
---|---|---|---|---|---|
MLPMixerS32 | 19.1M | 68.70 | |||
MLPMixerS16 | 18.5M | 73.83 | |||
MLPMixerB32 | 60.3M | 75.53 | b32_imagenet_sam.h5 | ||
MLPMixerB16 | 59.9M | 80.00 | b16_imagenet.h5 | b16_imagenet21k.h5 | b16_imagenet_sam.h5 |
MLPMixerL32 | 206.9M | 80.67 | |||
MLPMixerL16 | 208.2M | 84.82 | l16_imagenet.h5 | l16_imagenet21k.h5 | |
- input 448 | 208.2M | 86.78 | |||
MLPMixerH14 | 432.3M | 86.32 | |||
- input 448 | 432.3M | 87.94 |
- Keras MobileNetV3 includes implementation of PDF 1905.02244 Searching for MobileNetV3.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
MobileNetV3Small050 | 1.29M | 224 | 57.89 | small_050_imagenet.h5 |
MobileNetV3Small075 | 2.04M | 224 | 65.24 | small_075_imagenet.h5 |
MobileNetV3Small100 | 2.54M | 224 | 67.66 | small_100_imagenet.h5 |
MobileNetV3Large075 | 3.99M | 224 | 73.44 | large_075_imagenet.h5 |
MobileNetV3Large100 | 5.48M | 224 | 75.77 | large_100_imagenet.h5 |
- miil | 5.48M | 224 | 77.92 | large_100_miil.h5 |
- Keras MobileViT is for PDF 2110.02178 MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
MobileViT_XXS | 1.3M | 256 | 69.0 | mobilevit_xxs_imagenet |
MobileViT_XS | 2.3M | 256 | 74.7 | mobilevit_xs_imagenet |
MobileViT_S | 5.6M | 256 | 78.3 | mobilevit_s_imagenet |
- Keras NFNets is for PDF 2102.06171 High-Performance Large-Scale Image Recognition Without Normalization.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
NFNetL0 | 35.07M | 288 | 82.75 | nfnetl0_imagenet.h5 |
NFNetF0 | 71.5M | 256 | 83.6 | nfnetf0_imagenet.h5 |
NFNetF1 | 132.6M | 320 | 84.7 | nfnetf1_imagenet.h5 |
NFNetF2 | 193.8M | 352 | 85.1 | nfnetf2_imagenet.h5 |
NFNetF3 | 254.9M | 416 | 85.7 | nfnetf3_imagenet.h5 |
NFNetF4 | 316.1M | 512 | 85.9 | nfnetf4_imagenet.h5 |
NFNetF5 | 377.2M | 544 | 86.0 | nfnetf5_imagenet.h5 |
NFNetF6 SAM | 438.4M | 576 | 86.5 | nfnetf6_imagenet.h5 |
NFNetF7 | 499.5M | 608 | ||
ECA_NFNetL0 | 24.14M | 288 | 82.58 | eca_nfnetl0_imagenet.h5 |
ECA_NFNetL1 | 41.41M | 320 | 84.01 | eca_nfnetl1_imagenet.h5 |
ECA_NFNetL2 | 56.72M | 384 | 84.70 | eca_nfnetl2_imagenet.h5 |
ECA_NFNetL3 | 72.04M | 448 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
RegNetY040 | 20.65M | 224 | 82.3 | regnety_040_imagenet.h5 |
RegNetY064 | 30.58M | 224 | 83.0 | regnety_064_imagenet.h5 |
RegNetY080 | 39.18M | 224 | 83.17 | regnety_080_imagenet.h5 |
RegNetY160 | 83.59M | 224 | 82.0 | regnety_160_imagenet.h5 |
RegNetY320 | 145.05M | 224 | 82.5 | regnety_320_imagenet.h5 |
- Keras RegNetZ includes implementation of Github timm/models/byobnet.py.
- Related paper PDF 2004.02967 Evolving Normalization-Activation Layers
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
RegNetZB16 | 9.72M | 224 | 79.868 | regnetz_b16_imagenet.h5 |
RegNetZC16 | 13.46M | 256 | 82.164 | regnetz_c16_imagenet.h5 |
RegNetZC16_EVO | 13.49M | 256 | 81.9 | regnetz_c16_evo_imagenet.h5 |
RegNetZD32 | 27.58M | 256 | 83.422 | regnetz_d32_imagenet.h5 |
RegNetZD8 | 23.37M | 256 | 83.5 | regnetz_d8_imagenet.h5 |
RegNetZD8_EVO | 23.46M | 256 | 83.42 | regnetz_d8_evo_imagenet.h5 |
RegNetZE8 | 57.70M | 256 | 84.5 | regnetz_e8_imagenet.h5 |
- Keras ResMLP includes implementation of PDF 2105.03404 ResMLP: Feedforward networks for image classification with data-efficient training.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
ResMLP12 | 15M | 224 | 77.8 | resmlp12_imagenet.h5 |
ResMLP24 | 30M | 224 | 80.8 | resmlp24_imagenet.h5 |
ResMLP36 | 116M | 224 | 81.1 | resmlp36_imagenet.h5 |
ResMLP_B24 | 129M | 224 | 83.6 | resmlp_b24_imagenet.h5 |
- imagenet22k | 129M | 224 | 84.4 | resmlp_b24_imagenet22k.h5 |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
resnest50 | 28M | 224 | 81.03 | resnest50.h5 |
resnest101 | 49M | 256 | 82.83 | resnest101.h5 |
resnest200 | 71M | 320 | 83.84 | resnest200.h5 |
resnest269 | 111M | 416 | 84.54 | resnest269.h5 |
- Keras ResNetD includes implementation of PDF 1812.01187 Bag of Tricks for Image Classification with Convolutional Neural Networks
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
ResNet50D | 25.58M | 224 | 80.530 | resnet50d.h5 |
ResNet101D | 44.57M | 224 | 83.022 | resnet101d.h5 |
ResNet152D | 60.21M | 224 | 83.680 | resnet152d.h5 |
ResNet200D | 64.69M | 224 | 83.962 | resnet200d.h5 |
- Keras ResNetQ includes implementation of Github timm/models/resnet.py
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
ResNet51Q | 35.7M | 224 | 82.36 | resnet51q.h5 |
- Keras ResNeXt includes implementation of PDF 1611.05431 Aggregated Residual Transformations for Deep Neural Networks.
SWSL
meansSemi-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 | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
ResNeXt50 (32x4d) | 25M | 224 | 79.768 | resnext50_imagenet.h5 |
- SWSL | 25M | 224 | 82.182 | resnext50_swsl.h5 |
ResNeXt50D (32x4d + deep) | 25M | 224 | 79.676 | resnext50d_imagenet.h5 |
ResNeXt101 (32x4d) | 42M | 224 | 80.334 | resnext101_imagenet.h5 |
- SWSL | 42M | 224 | 83.230 | resnext101_swsl.h5 |
ResNeXt101W (32x8d) | 89M | 224 | 79.308 | resnext101_imagenet.h5 |
- SWSL | 89M | 224 | 84.284 | resnext101w_swsl.h5 |
- Keras SwinTransformerV2 includes implementation of PDF 2111.09883 Swin Transformer V2: Scaling Up Capacity and Resolution.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
SwinTransformerV2Tiny_ns | 28.3M | 224 | 81.8 | v2_tiny_ns_224_imagenet.h5 |
SwinTransformerV2Small | 49.7M | 224 | 83.13 | v2_small_224_imagenet.h5 |
SwinTransformerV2Base, 22k | 87.9M | 384 | 87.1 | |
SwinTransformerV2Large, 22k | 196.7M | 384 | 87.7 | |
SwinTransformerV2Giant, 22k+ext | 2.60B | 640 | 90.17 |
- Keras TinyNet includes implementation of PDF 2010.14819 Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
TinyNetE | 2.04M | 106 | 59.86 | tinynet_e_imagenet.h5 |
TinyNetD | 2.34M | 152 | 66.96 | tinynet_d_imagenet.h5 |
TinyNetC | 2.46M | 184 | 71.23 | tinynet_c_imagenet.h5 |
TinyNetB | 3.73M | 188 | 74.98 | tinynet_b_imagenet.h5 |
TinyNetA | 6.19M | 192 | 77.65 | tinynet_a_imagenet.h5 |
- Keras UniFormer includes implementation of PDF 2201.09450 UniFormer: Unifying Convolution and Self-attention for Visual Recognition.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
UniformerSmall32 + TL | 22M | 224 | 83.4 | small_32_224_token_label |
UniformerSmall64 | 22M | 224 | 82.9 | small_64_imagenet |
- Token Labeling | 22M | 224 | 83.4 | small_64_token_label |
UniformerSmallPlus32 | 24M | 224 | 83.4 | small_plus_32_imagenet |
- Token Labeling | 24M | 224 | 83.9 | small_plus_32_token_label |
UniformerSmallPlus64 | 24M | 224 | 83.4 | small_plus_64_imagenet |
- Token Labeling | 24M | 224 | 83.6 | small_plus_64_token_label |
UniformerBase32 + TL | 50M | 224 | 85.1 | base_32_224_token_label |
UniformerBase64 | 50M | 224 | 83.8 | base_64_imagenet |
- Token Labeling | 50M | 224 | 84.8 | base_64_224_token_label |
UniformerLarge64 + TL | 100M | 224 | 85.6 | large_64_224_token_label |
UniformerLarge64 + TL | 100M | 384 | 86.3 | large_64_384_token_label |
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
VOLO_d1 | 27M | 224 | 84.2 | volo_d1_224_imagenet.h5 |
VOLO_d1 ↑384 | 27M | 384 | 85.2 | volo_d1_384_imagenet.h5 |
VOLO_d2 | 59M | 224 | 85.2 | volo_d2_224_imagenet.h5 |
VOLO_d2 ↑384 | 59M | 384 | 86.0 | volo_d2_384_imagenet.h5 |
VOLO_d3 | 86M | 224 | 85.4 | volo_d3_224_imagenet.h5 |
VOLO_d3 ↑448 | 86M | 448 | 86.3 | volo_d3_448_imagenet.h5 |
VOLO_d4 | 193M | 224 | 85.7 | volo_d4_224_imagenet.h5 |
VOLO_d4 ↑448 | 193M | 448 | 86.8 | volo_d4_448_imagenet.h5 |
VOLO_d5 | 296M | 224 | 86.1 | volo_d5_224_imagenet.h5 |
VOLO_d5 ↑448 | 296M | 448 | 87.0 | volo_d5_448_imagenet.h5 |
VOLO_d5 ↑512 | 296M | 512 | 87.1 | volo_d5_512_imagenet.h5 |
- Keras WaveMLP includes implementation of PDF 2111.12294 An Image Patch is a Wave: Quantum Inspired Vision MLP.
Model | Params | Image resolution | Top1 Acc | Download |
---|---|---|---|---|
WaveMLP_T | 17M | 224 | 80.9 | wavemlp_t_imagenet.h5 |
WaveMLP_S | 30M | 224 | 82.9 | wavemlp_s_imagenet.h5 |
WaveMLP_M | 44M | 224 | 83.3 | wavemlp_m_imagenet.h5 |
WaveMLP_B | 63M | 224 | 83.6 |
- 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 | Image resolution | COCO val AP | test AP | Download |
---|---|---|---|---|---|
EfficientDetD0 | 3.9M | 512 | 34.3 | 34.6 | efficientdet_d0.h5 |
- Det-AdvProp | 3.9M | 512 | 35.1 | 35.3 | |
EfficientDetD1 | 6.6M | 640 | 40.2 | 40.5 | efficientdet_d1.h5 |
- Det-AdvProp | 6.6M | 640 | 40.8 | 40.9 | |
EfficientDetD2 | 8.1M | 768 | 43.5 | 43.9 | efficientdet_d2.h5 |
- Det-AdvProp | 8.1M | 768 | 44.3 | 44.3 | |
EfficientDetD3 | 12.0M | 896 | 46.8 | 47.2 | efficientdet_d3.h5 |
- Det-AdvProp | 12.0M | 896 | 47.7 | 48.0 | |
EfficientDetD4 | 20.7M | 1024 | 49.3 | 49.7 | efficientdet_d4.h5 |
- Det-AdvProp | 20.7M | 1024 | 50.4 | 50.4 | |
EfficientDetD5 | 33.7M | 1280 | 51.2 | 51.5 | efficientdet_d5.h5 |
- Det-AdvProp | 33.7M | 1280 | 52.2 | 52.5 | |
EfficientDetD6 | 51.9M | 1280 | 52.1 | 52.6 | efficientdet_d6.h5 |
EfficientDetD7 | 51.9M | 1536 | 53.4 | 53.7 | efficientdet_d7.h5 |
EfficientDetD7X | 77.0M | 1536 | 54.4 | 55.1 | efficientdet_d7x.h5 |
EfficientDetLite0 | 3.2M | 320 | 26.41 | efficientdet_lite0.h5 | |
EfficientDetLite1 | 4.2M | 384 | 31.50 | efficientdet_lite1.h5 | |
EfficientDetLite2 | 5.3M | 448 | 35.06 | efficientdet_lite2.h5 | |
EfficientDetLite3 | 8.4M | 512 | 38.77 | efficientdet_lite3.h5 | |
EfficientDetLite3X | 9.3M | 640 | 42.64 | efficientdet_lite3x.h5 | |
EfficientDetLite4 | 15.1M | 640 | 43.18 | efficientdet_lite4.h5 |
- Keras YOLOR includes implementation of Paper 2105.04206 You Only Learn One Representation: Unified Network for Multiple Tasks.
Model | Params | Image resolution | COCO val AP | test AP | Download |
---|---|---|---|---|---|
YOLOR_CSP | 52.9M | 640 | 50.0 | 52.8 | yolor_csp_coco.h5 |
YOLOR_CSPX | 99.8M | 640 | 51.5 | 54.8 | yolor_csp_x_coco.h5 |
YOLOR_P6 | 37.3M | 1280 | 52.5 | 55.7 | yolor_p6_coco.h5 |
YOLOR_W6 | 79.9M | 1280 | 56.9 | yolor_w6_coco.h5 | |
YOLOR_E6 | 115.9M | 1280 | 57.6 | yolor_e6_coco.h5 | |
YOLOR_D6 | 151.8M | 1280 | 58.2 | yolor_d6_coco.h5 |
- Keras YOLOX includes implementation of Paper 2107.08430 YOLOX: Exceeding YOLO Series in 2021.
Model | Params | Image resolution | COCO val AP | test AP | Download |
---|---|---|---|---|---|
YOLOXNano | 0.91M | 416 | 25.8 | yolox_nano_coco.h5 | |
YOLOXTiny | 5.06M | 416 | 32.8 | yolox_tiny_coco.h5 | |
YOLOXS | 9.0M | 640 | 40.5 | 40.5 | yolox_s_coco.h5 |
YOLOXM | 25.3M | 640 | 46.9 | 47.2 | yolox_m_coco.h5 |
YOLOXL | 54.2M | 640 | 49.7 | 50.1 | yolox_l_coco.h5 |
YOLOXX | 99.1M | 640 | 51.5 | 51.5 | yolox_x_coco.h5 |
- Github faustomorales/vit-keras
- Github rishigami/Swin-Transformer-TF
- Github tensorflow/resnet_rs
- Github google-research/big_transfer
- perceiver_image_classification