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Switch presets to final Kaggle location (#2216)
* Convert initial kaggle handles * Add Vision transformer kaggle switch * Add kagglehub requirement and auth, fix formatting * Special case input shape arg * Add kaggle username and key to CI * Add kaggle authentication to Keras 2 actions * Add test line * Add an extra space * Fix input shape build on load_preset * Fix format issues
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-84
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22 files changed

+102
-84
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‎.github/workflows/actions.yml‎

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@@ -41,6 +41,8 @@ jobs:
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pip install -e ".[tests]" --progress-bar off --upgrade
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- name: Test with pytest
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env:
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KAGGLE_USERNAME: ${{ secrets.KAGGLE_USERNAME }}
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KAGGLE_KEY: ${{ secrets.KAGGLE_KEY }}
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TEST_CUSTOM_OPS: false
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run: |
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pytest keras_cv/ --ignore keras_cv/models/legacy/ --durations 0
@@ -77,6 +79,8 @@ jobs:
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env:
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TEST_CUSTOM_OPS: false # TODO(ianstenbit): test custom ops, or figure out what our story is here
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KERAS_BACKEND: ${{ matrix.backend }}
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KAGGLE_USERNAME: ${{ secrets.KAGGLE_USERNAME}}
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KAGGLE_KEY: ${{ secrets.KAGGLE_KEY}}
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JAX_ENABLE_X64: true
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run: |
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pytest keras_cv/bounding_box \

‎cloudbuild/cloudbuild.yaml‎

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@@ -50,6 +50,7 @@ steps:
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'--ext-str', 'backend=$_BACKEND',
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'-o', 'output.yaml',
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]
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secretEnv: ['KAGGLE_USERNAME', 'KAGGLE_KEY']
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- name: 'gcr.io/cloud-builders/gcloud'
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id: create-job
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waitFor:
@@ -75,3 +76,9 @@ options:
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volumes:
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- name: go-modules
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path: /go
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availableSecrets:
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secretManager:
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- versionName: projects/$PROJECT_ID/secrets/KAGGLE_USERNAME/versions/latest
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env: 'KAGGLE_USERNAME'
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- versionName: projects/$PROJECT_ID/secrets/KAGGLE_KEY/versions/latest
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env: 'KAGGLE_KEY'

‎keras_cv/models/backbones/csp_darknet/csp_darknet_backbone_presets.py‎

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@@ -26,7 +26,7 @@
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_tiny",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_tiny/1",
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},
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"csp_darknet_s": {
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"metadata": {
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_s",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_s/1",
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},
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"csp_darknet_m": {
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"metadata": {
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_m",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_m/1",
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},
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"csp_darknet_l": {
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"metadata": {
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_l",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_l/1",
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},
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"csp_darknet_xl": {
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"metadata": {
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_xl",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_xl/1",
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},
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}
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@@ -95,7 +95,7 @@
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_tiny_imagenet",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_tiny_imagenet/1", # noqa: E501
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},
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"csp_darknet_l_imagenet": {
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"metadata": {
@@ -109,7 +109,7 @@
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"official_name": "CSPDarkNet",
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"path": "csp_darknet",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/csp_darknet_l_imagenet",
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"kaggle_handle": "kaggle://keras/cspdarknet/csp_darknet_l_imagenet/1",
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},
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}
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‎keras_cv/models/backbones/csp_darknet/csp_darknet_backbone_test.py‎

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@@ -113,6 +113,9 @@ def test_saved_alias_model(self):
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)
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def test_feature_pyramid_inputs(self):
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import os
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print("HERE", os.environ["KAGGLE_USERNAME"])
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model = CSPDarkNetLBackbone()
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backbone_model = get_feature_extractor(
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model,

‎keras_cv/models/backbones/densenet/densenet_backbone_presets.py‎

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@@ -18,19 +18,19 @@
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"metadata": {
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"description": "DenseNet model with 121 layers.",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet121",
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"kaggle_handle": "kaggle://keras/densenet/densenet121/1",
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},
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"densenet169": {
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"metadata": {
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"description": "DenseNet model with 169 layers.",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet169",
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"kaggle_handle": "kaggle://keras/densenet/densenet169/1",
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},
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"densenet201": {
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"metadata": {
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"description": "DenseNet model with 201 layers.",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet201",
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"kaggle_handle": "kaggle://keras/densenet/densenet201/1",
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},
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}
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@@ -42,7 +42,7 @@
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"classification task."
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),
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet121_imagenet",
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"kaggle_handle": "kaggle://keras/densenet/densenet121_imagenet/1",
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},
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"densenet169_imagenet": {
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"metadata": {
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"classification task."
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),
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet169_imagenet",
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"kaggle_handle": "kaggle://keras/densenet/densenet169_imagenet/1",
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},
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"densenet201_imagenet": {
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"metadata": {
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"classification task."
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),
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},
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"kaggle_handle": "gs://keras-cv-kaggle/densenet201_imagenet",
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"kaggle_handle": "kaggle://keras/densenet/densenet201_imagenet/1",
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},
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}
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‎keras_cv/models/backbones/efficientnet_v2/efficientnet_v2_backbone_presets.py‎

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@@ -24,7 +24,7 @@
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_s",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_s/1",
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},
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"efficientnetv2_m": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_m",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_m/1",
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},
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"efficientnetv2_l": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_l",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_l/1",
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},
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"efficientnetv2_b0": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b0",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b0/1",
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},
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"efficientnetv2_b1": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b1",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b1/1",
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},
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"efficientnetv2_b2": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b2",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b2/1",
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},
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"efficientnetv2_b3": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b3",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b3/1",
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},
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}
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_s_imagenet",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_s_imagenet/1", # noqa: E501
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},
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"efficientnetv2_b0_imagenet": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b0_imagenet",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b0_imagenet/1", # noqa: E501
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},
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"efficientnetv2_b1_imagenet": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b1_imagenet",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b1_imagenet/1", # noqa: E501
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},
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"efficientnetv2_b2_imagenet": {
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"metadata": {
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"official_name": "EfficientNetV2",
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"path": "efficientnetv2",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/efficientnetv2_b2_imagenet",
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"kaggle_handle": "kaggle://keras/efficientnetv2/efficientnetv2_b2_imagenet/1", # noqa: E501
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},
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}
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‎keras_cv/models/backbones/mix_transformer/mix_transformer_backbone_presets.py‎

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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b0",
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"kaggle_handle": "kaggle://keras/mit/mit_b0/1",
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},
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"mit_b1": {
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"metadata": {
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b1",
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"kaggle_handle": "kaggle://keras/mit/mit_b1/1",
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},
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"mit_b2": {
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"metadata": {
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b2",
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"kaggle_handle": "kaggle://keras/mit/mit_b2/1",
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},
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"mit_b3": {
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"metadata": {
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b3",
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"kaggle_handle": "kaggle://keras/mit/mit_b3/1",
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},
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"mit_b4": {
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"metadata": {
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b4",
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"kaggle_handle": "kaggle://keras/mit/mit_b4/1",
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},
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"mit_b5": {
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"metadata": {
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b5",
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"kaggle_handle": "kaggle://keras/mit/mit_b5/1",
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},
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}
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"official_name": "MiT",
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"path": "mit",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mit_b0_imagenet",
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"kaggle_handle": "kaggle://keras/mit/mit_b0_imagenet/1",
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},
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}
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‎keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone_presets.py‎

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"official_name": "MobileNetV3",
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"path": "mobilenetv3",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mobilenet_v3_small",
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"kaggle_handle": "kaggle://keras/mobilenetv3/mobilenet_v3_small/1",
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},
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"mobilenet_v3_large": {
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"metadata": {
@@ -38,7 +38,7 @@
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"official_name": "MobileNetV3",
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"path": "mobilenetv3",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mobilenet_v3_large",
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"kaggle_handle": "kaggle://keras/mobilenetv3/mobilenet_v3_large/1",
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},
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}
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@@ -55,7 +55,7 @@
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"official_name": "MobileNetV3",
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"path": "mobilenetv3",
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},
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"kaggle_handle": "gs://keras-cv-kaggle/mobilenet_v3_large_imagenet",
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"kaggle_handle": "kaggle://keras/mobilenetv3/mobilenet_v3_large_imagenet/1", # noqa: E501
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},
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"mobilenet_v3_small_imagenet": {
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"metadata": {
@@ -69,7 +69,7 @@
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"official_name": "MobileNetV3",
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"path": "mobilenetv3",
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},
72-
"kaggle_handle": "gs://keras-cv-kaggle/mobilenet_v3_small_imagenet",
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"kaggle_handle": "kaggle://keras/mobilenetv3/mobilenet_v3_small_imagenet/1", # noqa: E501
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},
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}
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