update

.github/workflows/main.yml

@@ -34,7 +34,7 @@ jobs:
         run: |
           coverage run -m pytest tests/common/
           coverage xml -o coverage-common.xml
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         with:
           name: coverage-common
           path: ./coverage-common.xml
@@ -67,7 +67,7 @@ jobs:
         run: |
           coverage run -m pytest tests/tensorflow/
           coverage xml -o coverage-tf.xml
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         with:
           name: coverage-tf
           path: ./coverage-tf.xml
@@ -115,7 +115,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/download-artifact@v4
       - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v3
+        uses: codecov/codecov-action@v4
         with:
           flags: unittests
           fail_ci_if_error: true

.github/workflows/references.yml

@@ -263,7 +263,7 @@ jobs:
           pip install -e .[torch,viz,html] --upgrade
       - if: matrix.framework == 'tensorflow'
         name: Benchmark latency (TF)
-        run: TF_USE_LEGACY_KERAS=1 python references/recognition/latency_tensorflow.py crnn_mobilenet_v3_small --it 5
+        run: python references/recognition/latency_tensorflow.py crnn_mobilenet_v3_small --it 5
       - if: matrix.framework == 'pytorch'
         name: Benchmark latency (PT)
         run: python references/recognition/latency_pytorch.py crnn_mobilenet_v3_small --it 5
@@ -408,7 +408,7 @@ jobs:
           pip install -e .[torch,viz,html] --upgrade
       - if: matrix.framework == 'tensorflow'
         name: Benchmark latency (TF)
-        run: TF_USE_LEGACY_KERAS=1 python references/detection/latency_tensorflow.py db_mobilenet_v3_large --it 5 --size 512
+        run: python references/detection/latency_tensorflow.py db_mobilenet_v3_large --it 5 --size 512
       - if: matrix.framework == 'pytorch'
         name: Benchmark latency (PT)
         run: python references/detection/latency_pytorch.py db_mobilenet_v3_large --it 5 --size 512

pyproject.toml

@@ -52,6 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 tf = [
+    # cf. https://github.com/mindee/doctr/pull/1461
     "tensorflow>=2.15.0",
     "tf-keras>=2.15.0",  # Keep keras 2 compatibility
     "tf2onnx>=1.16.0,<2.0.0",  # cf. https://github.com/onnx/tensorflow-onnx/releases/tag/v1.16.0
@@ -93,6 +94,7 @@ docs = [
 ]
 dev = [
     # Tensorflow
+    # cf. https://github.com/mindee/doctr/pull/1461
     "tensorflow>=2.15.0",
     "tf-keras>=2.15.0",  # Keep keras 2 compatibility
     "tf2onnx>=1.16.0,<2.0.0",  # cf. https://github.com/onnx/tensorflow-onnx/releases/tag/v1.16.0

references/classification/README.md

@@ -30,7 +30,7 @@ python references/classification/train_pytorch_character.py mobilenet_v3_large -
 You can start your training in TensorFlow:
 
 ```shell
-TF_USE_LEGACY_KERAS=1 python references/classification/train_tensorflow_orientation.py path/to/your/train_set path/to/your/val_set resnet18 page --epochs 5
+python references/classification/train_tensorflow_orientation.py path/to/your/train_set path/to/your/val_set resnet18 page --epochs 5
 ```
 
 or PyTorch:
@@ -61,7 +61,7 @@ Feel free to inspect the multiple script option to customize your training to yo
 Character classification:
 
 ```shell
-TF_USE_LEGACY_KERAS=1 python references/classification/train_tensorflow_character.py --help
+python references/classification/train_tensorflow_character.py --help
 ```
 
 Orientation classification:

references/classification/latency_tensorflow.py

@@ -9,14 +9,13 @@
 import os
 import time
 
-import numpy as np
-import tensorflow as tf
-
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-from doctr.models import classification
+from doctr.models import classification  # isort: skip
+
+import numpy as np
+import tensorflow as tf
 
 
 def main(args):

references/classification/train_tensorflow_character.py

@@ -6,7 +6,6 @@
 import os
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import datetime

references/classification/train_tensorflow_orientation.py

@@ -6,7 +6,6 @@
 import os
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import datetime

references/detection/README.md

@@ -16,7 +16,7 @@ pip install -r references/requirements.txt
 You can start your training in TensorFlow:
 
 ```shell
-TF_USE_LEGACY_KERAS=1 python references/detection/train_tensorflow.py path/to/your/train_set path/to/your/val_set db_resnet50 --epochs 5
+python references/detection/train_tensorflow.py path/to/your/train_set path/to/your/val_set db_resnet50 --epochs 5
 ```
 
 or PyTorch:

references/detection/evaluate_tensorflow.py

@@ -8,7 +8,6 @@
 from doctr.file_utils import CLASS_NAME
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import multiprocessing as mp

references/detection/latency_tensorflow.py

@@ -9,14 +9,13 @@
 import os
 import time
 
-import numpy as np
-import tensorflow as tf
-
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-from doctr.models import detection
+from doctr.models import detection  # isort: skip
+
+import numpy as np
+import tensorflow as tf
 
 
 def main(args):

references/detection/train_tensorflow.py

@@ -6,7 +6,6 @@
 import os
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import datetime

references/recognition/README.md

@@ -16,7 +16,7 @@ pip install -r references/requirements.txt
 You can start your training in TensorFlow:
 
 ```shell
-TF_USE_LEGACY_KERAS=1 python references/recognition/train_tensorflow.py crnn_vgg16_bn --train_path path/to/your/train_set --val_path path/to/your/val_set  --epochs 5
+python references/recognition/train_tensorflow.py crnn_vgg16_bn --train_path path/to/your/train_set --val_path path/to/your/val_set  --epochs 5
 ```
 
 or PyTorch:

references/recognition/evaluate_tensorflow.py

@@ -6,7 +6,6 @@
 import os
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import multiprocessing as mp

references/recognition/latency_tensorflow.py

@@ -9,14 +9,13 @@
 import os
 import time
 
-import numpy as np
-import tensorflow as tf
-
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-from doctr.models import recognition
+from doctr.models import recognition  # isort: skip
+
+import numpy as np
+import tensorflow as tf
 
 
 def main(args):

references/recognition/train_tensorflow.py

@@ -6,7 +6,6 @@
 import os
 
 os.environ["USE_TF"] = "1"
-os.environ["TF_USE_LEGACY_KERAS"] = "1"  # docTR requires Keras v2
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import datetime