Models

An INT8-quantized GoogLeNet image classification model based on an Inception-style convolutional architecture. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

This is an INT8-quantized version of YOLO11n optimized for edge deployment on ARM devices using ExecuTorch with the XNNPACK backend. The model was quantized using PT2E static symmetric per-channel quantization and exported to the .pte format for efficient inference on ARM Cortex-A processors (AWS Graviton, mobile ARM, embedded, Raspberry Pi).

This is an INT8-quantized version of yolo11n-pose (Ultralytics) optimized for edge deployment on ARM devices using ExecuTorch with the XNNPACK backend. The model was quantized using PT2E static quantization and exported to the .pte format for efficient inference on ARM Cortex-A processors (Raspberry Pi 5, mobile ARM, embedded).

An INT8-quantized YAMNet audio classification model for recognizing sound events. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

INT8-quantized 18-layer residual network for 1000-class image classification, optimized for efficient ARM edge inference with ExecuTorch and the XNNPACK backend. It uses 224×224 RGB inputs with ImageNet normalization and returns logits for top-k prediction, offering a much smaller, faster, and lower-memory alternative to the FP32 baseline while retaining comparable classification quality.

This is an INT8-quantized version of YOLOX-s optimized for edge deployment on ARM devices using ExecuTorch with the XNNPACK backend. The model was quantized using PT2E static quantization and exported to the .pte format for efficient inference on ARM Cortex-A processors (AWS Graviton, mobile ARM, embedded).

An INT8-quantized TinyLlama 1.1B Chat causal language model for conversational text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized GoogLeNet image classification model based on an Inception-style convolutional architecture. Exported as an ExecuTorch program using the Ethos-U delegate for inference on Arm Ethos-U NPUs.

An INT8-quantized YOLOX-S single-stage, anchor-free object detection model for locating and classifying objects in images. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

A 50-layer residual convolutional neural network for 1000-class image classification, using INT8 per-tensor quantization to reduce size and computation while preserving strong accuracy. It accepts 224×224 RGB inputs with ImageNet-style preprocessing and produces raw logits over 1,000 categories.

An INT8-quantized DETR object detection model combining a ResNet-50 backbone with a transformer-based detection architecture. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized monocular depth estimation model that predicts relative inverse-depth maps from single RGB images. It uses a ResNet-based backbone and produces dense depth outputs suitable for applications such as AR/VR, robotics, novel-view synthesis, and image editing. Inputs are resized RGB images, and outputs are relative depth maps requiring calibration for metric depth.

An INT8-quantized QuickSRNetSmall image super-resolution model for upscaling low-resolution images. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized Gemma 2 2B causal language model for text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized Qwen2.5 0.5B Instruct causal language model for instruction-following and text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized Phi-3.5 Mini language model for text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized BGE Base EN v1.5 English text embedding model for semantic search, retrieval, and similarity tasks. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized Llama 3.1 8B Instruct causal language model for instruction-following and text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized single-stage object detector for 80 COCO classes, using an anchor-free architecture with 640×640 RGB inputs. It outputs bounding boxes and class scores for common objects, with postprocessing via confidence filtering, box conversion, and per-class non-maximum suppression.

An INT8-quantized YOLOX-S single-stage, anchor-free object detection model for locating and classifying objects in images. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized Wide ResNet-50 image classification model using a widened residual convolutional network for visual feature extraction. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized, lightweight segment-anything image segmentation family with a compact TinyViT backbone, designed for box-prompted instance mask generation. It produces multiple candidate masks with IoU quality scores from RGB images and bounding-box prompts, offering reduced size and memory use while preserving high mask quality.

An INT8-quantized Phi-4 Mini language model for text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

An INT8-quantized TinyLlama 1.1B Chat causal language model for conversational text generation. Exported as an ExecuTorch program using the XNNPACK backend for inference on Arm CPUs.

A compact 362M-parameter decoder-only causal language model for instruction-following, open-ended text generation, and multiple-choice scoring, using 8-bit dynamic activations and 4-bit grouped weights to reduce size and memory needs while preserving core language capabilities.

An INT8 per-channel quantized 50-layer residual network for 1000-class image classification, using residual connections to support deep feature extraction while reducing model size and compute requirements. It accepts 224×224 RGB inputs with ImageNet-style normalization and outputs raw logits for class prediction.

INT8-quantized box-prompted image segmentation model with a ViT-Small backbone and lightweight mask decoder. It accepts a 1024×1024 RGB image plus bounding-box coordinates and returns a binary mask for the prompted region, offering reduced model size and faster inference while preserving segmentation quality.