- Keras implementation of ResNeSt. Paper PDF 2004.08955 ResNeSt: Split-Attention Networks.
- Model weights reloaded from official publication.
| 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 |
from keras_cv_attention_models import resnest
# Will download and load pretrained imagenet weights.
mm = resnest.ResNest50(pretrained="imagenet")
# Run prediction
import tensorflow as tf
from tensorflow import keras
from skimage.data import chelsea
imm = keras.applications.imagenet_utils.preprocess_input(chelsea(), mode='torch') # Chelsea the cat
pred = mm(tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)).numpy()
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.7793046),
# ('n02123159', 'tiger_cat', 0.028313603),
# ('n04209239', 'tabby', 0.02322878),
# ('n02127052', 'lynx', 0.0036637571),
# ('n03085013', 'computer_keyboard', 0.0008628946)]Use dynamic input resolution
from keras_cv_attention_models import resnest
mm = resnest.ResNest50(input_shape=(None, None, 3), num_classes=0)
print(mm(np.ones([1, 224, 224, 3])).shape)
# (1, 7, 7, 2048)
print(mm(np.ones([1, 512, 512, 3])).shape)
# (1, 16, 16, 2048)
mm.save("../models/resnest50_dynamic_notop.h5")""" PyTorch resnest50 """
import torch
sys.path.append("../")
from ResNeSt.resnest.torch import resnest as torch_resnest
torch_model = torch_resnest.resnest50(pretrained=True)
torch_model.eval()
""" Keras ResNest50 """
from keras_cv_attention_models import resnest
mm = resnest.ResNest50(pretrained="imagenet", classifier_activation=None)
""" Verification """
inputs = np.random.uniform(size=(1, *mm.input_shape[1:3], 3)).astype("float32")
torch_out = torch_model(torch.from_numpy(inputs).permute(0, 3, 1, 2)).detach().numpy()
keras_out = mm(inputs).numpy()
print(f"{np.allclose(torch_out, keras_out, atol=1e-4) = }")
# np.allclose(torch_out, keras_out, atol=1e-4) = True