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Describe the bug PVT model does not train.
To Reproduce Steps to reproduce the behaviour:
import tfimm import tensorflow_datasets as tfds import tensorflow as tf def resize_normalize(x, y): x = tf.image.resize(x, (224, 224)) / 255 return x, y train_ds = tfds.load('imagenet_v2', split='test', as_supervised=True) train_ds = train_ds.map(resize_normalize).batch(32) model = tfimm.create_model("pvt_tiny", pretrained=None) model.compile(optimizer=tf.keras.optimizers.Adam(1e-3), loss="sparse_categorical_crossentropy", metrics=["accuracy"]) model.fit(train_ds)
Epoch 1/5 313/313 [==============================] - 67s 187ms/step - loss: 15.6424 - accuracy: 6.0000e-04 Epoch 2/5 313/313 [==============================] - 60s 191ms/step - loss: 16.2130 - accuracy: 0.0010 Epoch 3/5 313/313 [==============================] - 60s 191ms/step - loss: 16.2144 - accuracy: 0.0010 Epoch 4/5 313/313 [==============================] - 60s 191ms/step - loss: 16.2418 - accuracy: 0.0010 Epoch 5/5 313/313 [==============================] - 60s 191ms/step - loss: 16.2417 - accuracy: 0.0010
Expected behaviour Convergance of model
Desktop (please complete the following information):
Also note that setting the LR to 1e-4 as the paper does not solve the problem.
1e-4
The text was updated successfully, but these errors were encountered:
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Describe the bug
PVT model does not train.
To Reproduce
Steps to reproduce the behaviour:
Expected behaviour
Convergance of model
Desktop (please complete the following information):
Also note that setting the LR to
1e-4
as the paper does not solve the problem.The text was updated successfully, but these errors were encountered: