Releases: cocoa-xu/tflite_beam
Releases · cocoa-xu/tflite_beam
v0.3.7
v0.3.6
Changelog
v0.3.6 (2024-03-17)
Browse the Repository | Released Assets
Changed
- [deps] Use libedgetpu v0.1.9.
- Use tensorflow v2.16.1.
- Use libusb v1.0.27.
- Use Erlang/OTP 25.x for precompiled binaries. This unified the required Erlang/OTP NIF version to
2.16
for precompiled binaries. - Detect and use
HTTP_PROXY
,HTTPS_PROXY
,http_proxy
andhttps_proxy
when fetch preocmpiled binary from GitHub.
v0.3.5
v0.3.5 (2024-01-24)
Browse the Repository | Released Assets
Changed
- Precompiled version for armv6 devices.
- Removed
TFBEAM_XNNPACK_ENABLE_ARM_I8MM
option as it should work as long as a newer C compiler is used. - Updated metadata_schema to 1.5.0
v0.3.4
v0.3.4 (2024-01-23)
Browse the Repository | Released Assets
Changed
- [deps] Use libedgetpu v0.1.8.
- Use tensorflow v2.15.0.
v0.3.3
fix ci
v0.3.2
v0.3.2
v0.3.1
updated scripts
v0.3.0
`@moduledoc` => `@doc`
v0.2.1
use TensorFlow 2.11.1
v0.2.0
Changelog
v0.2.0 (2022-03-30)
Browse the Repository | Released Assets
Breaking Changes
- Renamed root namespace from
TFLiteElixir
toTFLiteBEAM
Changes
buffer
will be copied and managed when usingTFLiteBEAM.FlatBufferModel.build_from_buffer/1
.TFLiteBEAM.TFLiteTensor.dims/1
returns a list (following TensorFlow Lite's C++ API convention) whileTFLiteBEAM.TFLiteTensor.shape/1
returns a tuple (folllowingnx
's convention.)
Added
-
Erlang support.
-
[example] added pose estimation example (#43) by @mnishiguchi
-
[example] use thunder model instead of lightning in pose estimation (#45) by @mnishiguchi
-
[example] added audio classification example
-
Experimental high-level module
TFLiteBEAM.ImageClassification
.iex> alias TFLiteBEAM.ImageClassification iex> {:ok, pid} = ImageClassification.start("test/test_data/mobilenet_v2_1.0_224_inat_bird_quant.tflite") iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg") %{class_id: 923, score: 0.70703125} iex> ImageClassification.set_label_from_associated_file(pid, "inat_bird_labels.txt") :ok iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg") %{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125} iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg", top_k: 3) [ %{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125}, %{ class_id: 837, label: "Platycercus elegans (Crimson Rosella)", score: 0.078125 }, %{ class_id: 245, label: "Coracias caudatus (Lilac-breasted Roller)", score: 0.01953125 } ]