EMO-AffectNet (VGGFace2 ResNet50) — Jetson Orin Nano FP16
7-class facial emotion classifier (Neutral / Happy / Sad / Surprise / Fear / Disgust / Anger). Keras .h5 + ONNX + TensorRT FP16 engine with dynamic batch (1–16).
This is a private mirror prepared for the robot_perception_scoring project (github.com/JSBAICenter/Nvidia_Edge_work_arounds, branch orin_yolo). It bundles the original source weights together with an ONNX export and a TensorRT FP16 engine built on a Jetson Orin Nano (8 GB).
Files in this repo
| Role | Filename | Size | SHA-256 |
|---|---|---|---|
keras_h5 |
weights_0_66_37_wo_gl.h5 |
188.4 MB | b9d403390b3a5938… |
onnx |
weights_0_66_37_wo_gl.onnx |
93.6 MB | 52e9c972d6d7728b… |
engine |
weights_0_66_37_wo_gl.engine |
47.5 MB | a3815d027124aabc… |
How to load
- keras_h5:
tf.keras.models.load_model('weights_0_66_37_wo_gl.h5') - onnx:
onnxruntime.InferenceSession(any CUDA-capable host) - engine:
tensorrt.Runtime.deserialize_cuda_engine(Jetson Orin Nano only)
The .engine artifact is hardware-specific to Jetson Orin Nano (Ampere sm_87, TensorRT 10.3, CUDA 12.6, FP16). It will not load on other Jetson families, other TensorRT versions, or x86 GPUs. Other users should rebuild from the .onnx via python -m tools.build_engines in the linked GitHub repo.
Build environment
- JetPack tegra release: # R36 (release), REVISION: 4.7, GCID: 42132812, BOARD: generic, EABI: aarch64, DATE: Thu Sep 18 22:54:44 UTC 2025
- CUDA: Cuda compilation tools, release 12.6, V12.6.68
- TensorRT: 10.3.0
Attribution
EMO-AffectNet trained over the VGGFace2 ResNet50 backbone. Preprocessing matches keras-vggface preprocess_input(version=2): 224x224 BGR, per-channel mean subtraction (B-91.5, G-103.9, R-131.1).
License
Per the original EMO-AffectNet authors. Re-distribution beyond this private mirror should be cleared with them.
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