Qualcomm® AI Hub Models

The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for deployment on Qualcomm® devices.

See supported: On-Device Runtimes, Hardware Targets & Precision, Chipsets, Devices

NEW: Quick Start with the AI Hub Models CLI

Use our lightweight command-line interface to browse and download from the collection of Qualcomm® AI Hub Models.

pip install qai_hub_models_cli # (the CLI is also available with the qai-hub-models package)

qai-hub-models models                                                # browse the catalog
qai-hub-models info mobilenet_v2                                     # model details + download options
qai-hub-models fetch mobilenet_v2 --runtime tflite --precision float # download a deployable asset
# ... and more

The CLI also offers a Python API.

See the CLI README for full usage instructions.

Setup

1. Install Python Package

The package is available via pip:

# NOTE for Snapdragon X Elite and Snapdragon X2 Elite users:
# Only AMDx64 (64-bit) Python is supported on Windows.
# Installation will fail when using Windows ARM64 Python.

pip install qai_hub_models

Some models (e.g. YOLOv7) require additional dependencies. See the model's install instructions in the GitHub repository.

2. Configure AI Hub Workbench Access

Many features of AI Hub Models (such as model compilation, on-device profiling, etc.) require access to Qualcomm® AI Hub Workbench:

Create a Qualcomm® ID, and use it to login to Qualcomm® AI Hub Workbench.
Configure your API token: qai-hub configure --api_token API_TOKEN

Getting Started

Export and Run A Model on a Physical Device

All models in our directory can be compiled and profiled on a hosted Qualcomm® device:

pip install "qai_hub_models[yolov7]"

qai-hub-models export yolov7 --target-runtime tflite --precision float --device "Samsung Galaxy S25 (Family)"

Using Qualcomm® AI Hub Workbench, the export script will:

Compile the model for the chosen device and target runtime (see: Compiling Models on AI Hub Workbench).
If applicable, Quantize the model (see: Quantization on AI Hub Workbench)
Profile the compiled model on a real device in the cloud (see: Profiling Models on AI Hub Workbench).
Run inference with a sample input data on a real device in the cloud, and compare on-device model output with PyTorch output (see: Running Inference on AI Hub Workbench)
Download the compiled model to disk.

End-To-End Model Demos

Most models in our directory contain CLI demos that run the model end-to-end:

pip install "qai_hub_models[yolov7]"
# Predict and draw bounding boxes on the provided image
python -m qai_hub_models.models.yolov7.demo [--image ...] [--eval-mode {fp,on-device}] [--help]

End-to-end demos:

Preprocess human-readable input into model input
Run model inference
Postprocess model output to a human-readable format

Many end-to-end demos use AI Hub Workbench to run inference on a real cloud-hosted device (with --eval-mode on-device). All end-to-end demos can also run locally via PyTorch (with --eval-mode fp).

Sample Applications

Native applications that can run our models (with pre- and post-processing) on physical devices are published in the AI Hub Apps repository.

Python applications are defined for all models (from qai_hub_models.models.<model_name> import App). These apps wrap model inference with pre- and post-processing steps written using torch & numpy. These apps are optimized to be an easy-to-follow example, rather than to minimize prediction time.

Model Support Data

On-Device Runtimes

Runtime	Supported OS
Qualcomm AI Engine Direct	Android, Linux, Windows
LiteRT (TensorFlow Lite)	Android, Linux
ONNX	Android, Linux, Windows

Device Hardware & Precision

Device Compute Unit	Supported Precision
CPU	FP32, INT16, INT8
GPU	FP32, FP16
NPU (includes Hexagon DSP, HTP)	FP16*, INT16, INT8

*Some older chipsets do not support fp16 inference on their NPU.

Chipsets

Snapdragon 8 Elite Gen 5, 8 Elite, 8 Gen 3, 8 Gen 2, and 8 Gen 1 Mobile Platforms
Snapdragon X2 Elite, Snapdragon X Elite Compute Platforms
SA7255P, SA8295P, and SA8775P Automotive Platforms
QCS 6490, QCS 8250, QCS 9075, and QCS 8550 IoT Platforms
QCS8450 XR Platform

and many more.

Devices

Samsung Galaxy S21, S22, S23, S24, and S25 Series
Xiaomi 12, 13, 15, and 17
Snapdragon X Elite CRD and Snapdragon X2 Elite CRD (Compute Reference Device)
Qualcomm RB3 Gen 2, RB5 Gen 2, IQ-8, IQ-9

and many more.

Model Directory

Computer Vision

Model	Package

Image Classification
Beit	`qai_hub_models.models.beit`
ConvNext-Base	`qai_hub_models.models.convnext_base`
ConvNext-Tiny	`qai_hub_models.models.convnext_tiny`
DLA-102-X	`qai_hub_models.models.dla102x`
DenseNet-121	`qai_hub_models.models.densenet121`
EfficientFormer	`qai_hub_models.models.efficientformer`
EfficientNet-B0	`qai_hub_models.models.efficientnet_b0`
EfficientNet-B4	`qai_hub_models.models.efficientnet_b4`
EfficientNet-Lite4	`qai_hub_models.models.efficientnet_lite4`
EfficientNet-V2-s	`qai_hub_models.models.efficientnet_v2_s`
EfficientViT-b2-cls	`qai_hub_models.models.efficientvit_b2_cls`
EfficientViT-l2-cls	`qai_hub_models.models.efficientvit_l2_cls`
GPUNet	`qai_hub_models.models.gpunet`
GoogLeNet	`qai_hub_models.models.googlenet`
Inception-v3	`qai_hub_models.models.inception_v3`
InternImage	`qai_hub_models.models.internimage`
LeViT	`qai_hub_models.models.levit`
MNASNet05	`qai_hub_models.models.mnasnet05`
Mobile-VIT	`qai_hub_models.models.mobile_vit`
MobileNet-v2	`qai_hub_models.models.mobilenet_v2`
MobileNet-v3-Large	`qai_hub_models.models.mobilenet_v3_large`
MobileNet-v3-Small	`qai_hub_models.models.mobilenet_v3_small`
NASNet	`qai_hub_models.models.nasnet`
RegNet	`qai_hub_models.models.regnet`
RegNet-Y-800MF	`qai_hub_models.models.regnet_y_800mf`
ResNeXt101	`qai_hub_models.models.resnext101`
ResNeXt50	`qai_hub_models.models.resnext50`
ResNet101	`qai_hub_models.models.resnet101`
ResNet18	`qai_hub_models.models.resnet18`
ResNet50	`qai_hub_models.models.resnet50`
Sequencer2D	`qai_hub_models.models.sequencer2d`
Shufflenet-v2	`qai_hub_models.models.shufflenet_v2`
SqueezeNet-1.1	`qai_hub_models.models.squeezenet1_1`
Swin-Base	`qai_hub_models.models.swin_base`
Swin-Small	`qai_hub_models.models.swin_small`
Swin-Tiny	`qai_hub_models.models.swin_tiny`
SwinV2-Base	`qai_hub_models.models.swinv2_base`
VIT	`qai_hub_models.models.vit`
WideResNet50	`qai_hub_models.models.wideresnet50`

Image Editing
AOT-GAN	`qai_hub_models.models.aotgan`
DDColor	`qai_hub_models.models.ddcolor`
DnCNN	`qai_hub_models.models.dncnn`
LaMa-Dilated	`qai_hub_models.models.lama_dilated`
NAFNet-DeBlur	`qai_hub_models.models.nafnet_deblur`
NAFNet-DeNoise	`qai_hub_models.models.nafnet_denoise`

Super Resolution
ESRGAN	`qai_hub_models.models.esrgan`
NAFSSR	`qai_hub_models.models.nafssr`
QuickSRNetLarge	`qai_hub_models.models.quicksrnetlarge`
QuickSRNetMedium	`qai_hub_models.models.quicksrnetmedium`
QuickSRNetSmall	`qai_hub_models.models.quicksrnetsmall`
Real-ESRGAN-General-x4v3	`qai_hub_models.models.real_esrgan_general_x4v3`
Real-ESRGAN-x4plus	`qai_hub_models.models.real_esrgan_x4plus`
SESR-M5	`qai_hub_models.models.sesr_m5`
XLSR	`qai_hub_models.models.xlsr`

Semantic Segmentation
DDRNet23-Slim	`qai_hub_models.models.ddrnet23_slim`
DeepLabV3-Plus-MobileNet	`qai_hub_models.models.deeplabv3_plus_mobilenet`
DeepLabXception	`qai_hub_models.models.deeplab_xception`
EdgeTAM	`qai_hub_models.models.edgetam`
FCN-ResNet50	`qai_hub_models.models.fcn_resnet50`
FFNet-122NS-LowRes	`qai_hub_models.models.ffnet_122ns_lowres`
FFNet-40S	`qai_hub_models.models.ffnet_40s`
FFNet-54S	`qai_hub_models.models.ffnet_54s`
FFNet-78S	`qai_hub_models.models.ffnet_78s`
FFNet-78S-LowRes	`qai_hub_models.models.ffnet_78s_lowres`
FastSam-S	`qai_hub_models.models.fastsam_s`
FastSam-X	`qai_hub_models.models.fastsam_x`
HRNet-W48-OCR	`qai_hub_models.models.hrnet_w48_ocr`
Mask2Former	`qai_hub_models.models.mask2former`
MaskRCNN	`qai_hub_models.models.maskrcnn`
MediaPipe-Selfie-Segmentation	`qai_hub_models.models.mediapipe_selfie`
MobileSam	`qai_hub_models.models.mobilesam`
PSPNet	`qai_hub_models.models.pspnet`
PidNet	`qai_hub_models.models.pidnet`
PointNet	`qai_hub_models.models.pointnet`
SINet	`qai_hub_models.models.sinet`
SalsaNext	`qai_hub_models.models.salsanext`
Segformer-Base	`qai_hub_models.models.segformer_base`
Segment-Anything-Model-2	`qai_hub_models.models.sam2`
Segment-Anything-Model-3	`qai_hub_models.models.sam3`
Unet-Segmentation	`qai_hub_models.models.unet_segmentation`
YOLO26-Segmentation	`qai_hub_models.models.yolo26_seg`
YOLOE-Segmentation	`qai_hub_models.models.yoloe_seg`
YOLOv11-Segmentation	`qai_hub_models.models.yolov11_seg`
YOLOv8-Segmentation	`qai_hub_models.models.yolov8_seg`

Video Classification
ResNet-2Plus1D	`qai_hub_models.models.resnet_2plus1d`
ResNet-3D	`qai_hub_models.models.resnet_3d`
ResNet-Mixed-Convolution	`qai_hub_models.models.resnet_mixed`
Video-MAE	`qai_hub_models.models.video_mae`

Video Generation
First-Order-Motion-Model	`qai_hub_models.models.fomm`

Video Object Tracking
Track-Anything	`qai_hub_models.models.track_anything`

Object Detection
3D-Deep-BOX	`qai_hub_models.models.deepbox`
CavaFace	`qai_hub_models.models.cavaface`
CenterNet-2D	`qai_hub_models.models.centernet_2d`
Conditional-DETR-ResNet50	`qai_hub_models.models.conditional_detr_resnet50`
DETR-ResNet101	`qai_hub_models.models.detr_resnet101`
DETR-ResNet101-DC5	`qai_hub_models.models.detr_resnet101_dc5`
DETR-ResNet50	`qai_hub_models.models.detr_resnet50`
DETR-ResNet50-DC5	`qai_hub_models.models.detr_resnet50_dc5`
Detectron2-Detection	`qai_hub_models.models.detectron2_detection`
Facial-Attribute-Detection	`qai_hub_models.models.face_attrib_net`
HRNetFace	`qai_hub_models.models.hrnet_face`
Lightweight-Face-Detection	`qai_hub_models.models.face_det_lite`
MediaPipe-Face-Detection	`qai_hub_models.models.mediapipe_face`
MediaPipe-Hand-Detection	`qai_hub_models.models.mediapipe_hand`
MediaPipe-Hand-Gesture-Recognition	`qai_hub_models.models.mediapipe_hand_gesture`
PPE-Detection	`qai_hub_models.models.gear_guard_net`
Person-Foot-Detection	`qai_hub_models.models.foot_track_net`
RF-DETR	`qai_hub_models.models.rf_detr`
RTMDet	`qai_hub_models.models.rtmdet`
ResNet34-SSD	`qai_hub_models.models.resnet34_ssd1200`
YOLO-WORLD	`qai_hub_models.models.yolo_world`
YOLO26-Detection	`qai_hub_models.models.yolo26_det`
YOLOv10-Detection	`qai_hub_models.models.yolov10_det`
YOLOv11-Detection	`qai_hub_models.models.yolov11_det`
YOLOv8-Detection	`qai_hub_models.models.yolov8_det`
YOLOv8-OBB	`qai_hub_models.models.yolov8_obb`
YOLOv9-Detection	`qai_hub_models.models.yolov9_det`
Yolo-R	`qai_hub_models.models.yolor`
Yolo-X	`qai_hub_models.models.yolox`
Yolo-v3	`qai_hub_models.models.yolov3`
Yolo-v5	`qai_hub_models.models.yolov5`
Yolo-v6	`qai_hub_models.models.yolov6`
Yolo-v7	`qai_hub_models.models.yolov7`

Pose Estimation
CenterNet-Pose	`qai_hub_models.models.centernet_pose`
Facial-Landmark-Detection	`qai_hub_models.models.facemap_3dmm`
HRNetPose	`qai_hub_models.models.hrnet_pose`
LiteHRNet	`qai_hub_models.models.litehrnet`
MediaPipe-Pose-Estimation	`qai_hub_models.models.mediapipe_pose`
Posenet-Mobilenet	`qai_hub_models.models.posenet_mobilenet`
RTMPose-Body2d	`qai_hub_models.models.rtmpose_body2d`
SixDRepNet	`qai_hub_models.models.sixd_repnet`
YOLO26-Pose	`qai_hub_models.models.yolo26_pose`
YOLOv11-Pose	`qai_hub_models.models.yolov11_pose`

Gaze Estimation
EyeGaze	`qai_hub_models.models.eyegaze`

Depth Estimation
CREStereo	`qai_hub_models.models.crestereo`
Depth-Anything	`qai_hub_models.models.depth_anything`
Depth-Anything-V2	`qai_hub_models.models.depth_anything_v2`
Depth-Anything-V3	`qai_hub_models.models.depth_anything_v3`
Midas-V2	`qai_hub_models.models.midas`
StereoNet	`qai_hub_models.models.stereonet`

Driver Assistance
BEVDet	`qai_hub_models.models.bevdet`
BEVFusion	`qai_hub_models.models.bevfusion_det`
CVT	`qai_hub_models.models.cvt`
CenterNet-3D	`qai_hub_models.models.centernet_3d`
CenterPoint	`qai_hub_models.models.centerpoint`
GKT	`qai_hub_models.models.gkt`
RangeNet-Plus-Plus	`qai_hub_models.models.rangenet_plus_plus`
StateTransformer	`qai_hub_models.models.statetransformer`

Robotics
ACT	`qai_hub_models.models.act`

Multimodal

Model	Package

EasyOCR	`qai_hub_models.models.easyocr`
GR00TN1.5	`qai_hub_models.models.grootn15`
MiniLM-v2	`qai_hub_models.models.minilm_v2`
Nomic-Embed-Text	`qai_hub_models.models.nomic_embed_text`
OpenAI-Clip	`qai_hub_models.models.openai_clip`
OpusMT-En-Es	`qai_hub_models.models.opus_mt_en_es`
OpusMT-En-Zh	`qai_hub_models.models.opus_mt_en_zh`
OpusMT-Es-En	`qai_hub_models.models.opus_mt_es_en`
OpusMT-Zh-En	`qai_hub_models.models.opus_mt_zh_en`
Pi0.5	`qai_hub_models.models.pi05`
TrOCR	`qai_hub_models.models.trocr`

Audio

Model	Package

Speech Recognition
Distil-Whisper	`qai_hub_models.models.distil_whisper`
Whisper-Base	`qai_hub_models.models.whisper_base`
Whisper-Large-V3-Turbo	`qai_hub_models.models.whisper_large_v3_turbo`
Whisper-Medium	`qai_hub_models.models.whisper_medium`
Whisper-Small	`qai_hub_models.models.whisper_small`
Whisper-Small-Quantized	`qai_hub_models.models.whisper_small_quantized`
Whisper-Tiny	`qai_hub_models.models.whisper_tiny`
Zipformer	`qai_hub_models.models.zipformer`

Audio Classification
YamNet	`qai_hub_models.models.yamnet`

Audio Generation
MeloTTS-EN	`qai_hub_models.models.melotts_en`
MeloTTS-ES	`qai_hub_models.models.melotts_es`
MeloTTS-ZH	`qai_hub_models.models.melotts_zh`
PiperTTS-DE	`qai_hub_models.models.pipertts_de`
PiperTTS-EN	`qai_hub_models.models.pipertts_en`
PiperTTS-IT	`qai_hub_models.models.pipertts_it`

Generative AI

Model	Package

Image Generation
ControlNet-Canny	`qai_hub_models.models.controlnet_canny`
Stable-Diffusion-v1.5	`qai_hub_models.models.stable_diffusion_v1_5`
Stable-Diffusion-v2.1	`qai_hub_models.models.stable_diffusion_v2_1`

Text Generation
Albert-Base-V2-Hf	`qai_hub_models.models.albert_base_v2_hf`
Bert-Base-Uncased-Hf	`qai_hub_models.models.bert_base_uncased_hf`
Distil-Bert-Base-Uncased-Hf	`qai_hub_models.models.distil_bert_base_uncased_hf`
Electra-Bert-Base-Discrim-Google	`qai_hub_models.models.electra_bert_base_discrim_google`
Falcon3-7B-Instruct	`qai_hub_models.models.falcon_v3_7b_instruct`
GPT-OSS-20B	`qai_hub_models.models.gpt_oss_20b`
Gemma-4-E2B-it	`qai_hub_models.models.gemma_4_e2b_it`
Gemma-4-E4B-it	`qai_hub_models.models.gemma_4_e4b_it`
Granite-4.0-Micro	`qai_hub_models.models.granite_4_0_micro`
IBM-Granite-v3.1-8B-Instruct	`qai_hub_models.models.ibm_granite_v3_1_8b_instruct`
IndusQ-1.1B	`qai_hub_models.models.indus_1b`
JAIS-6p7b-Chat	`qai_hub_models.models.jais_6p7b_chat`
Llama-SEA-LION-v3.5-8B-R	`qai_hub_models.models.llama_v3_1_sea_lion_3_5_8b_r`
Llama-v3-8B-Instruct	`qai_hub_models.models.llama_v3_8b_instruct`
Llama-v3-ELYZA-JP-8B	`qai_hub_models.models.llama_v3_elyza_jp_8b`
Llama-v3.1-8B-Instruct	`qai_hub_models.models.llama_v3_1_8b_instruct`
Llama-v3.2-1B-Instruct	`qai_hub_models.models.llama_v3_2_1b_instruct`
Llama-v3.2-3B-Instruct	`qai_hub_models.models.llama_v3_2_3b_instruct`
Llama-v3.2-3B-Instruct-SSD	`qai_hub_models.models.llama_v3_2_3b_instruct_ssd`
Llama3-TAIDE-LX-8B-Chat-Alpha1	`qai_hub_models.models.llama_v3_taide_8b_chat`
Ministral-3-3B-Instruct-2512	`qai_hub_models.models.ministral_3_3b_instruct_2512`
Mistral-7B-Instruct-v0.3	`qai_hub_models.models.mistral_7b_instruct_v0_3`
Mobile-Bert-Uncased-Google	`qai_hub_models.models.mobile_bert_uncased_google`
PLaMo-1B	`qai_hub_models.models.plamo_1b`
Phi-3.5-Mini-Instruct	`qai_hub_models.models.phi_3_5_mini_instruct`
Phi-4-Mini-Instruct	`qai_hub_models.models.phi_4_mini_instruct`
Qwen2-7B-Instruct	`qai_hub_models.models.qwen2_7b_instruct`
Qwen2.5-VL-7B-Instruct	`qai_hub_models.models.qwen2_5_vl_7b_instruct`
Qwen3-0.6B	`qai_hub_models.models.qwen3_0_6b`
Qwen3-1.7B	`qai_hub_models.models.qwen3_1_7b`
Qwen3-4B	`qai_hub_models.models.qwen3_4b`
Qwen3-4B-Instruct-2507	`qai_hub_models.models.qwen3_4b_instruct_2507`
Qwen3-8B	`qai_hub_models.models.qwen3_8b`
Qwen3-VL-2B-Instruct	`qai_hub_models.models.qwen3_vl_2b_instruct`
Qwen3-VL-4B-Instruct	`qai_hub_models.models.qwen3_vl_4b_instruct`
Qwen3-VL-8B-Instruct	`qai_hub_models.models.qwen3_vl_8b_instruct`
Qwen3.5-0.8B	`qai_hub_models.models.qwen3_5_0_8b`
Qwen3.5-2B	`qai_hub_models.models.qwen3_5_2b`

Need help?

Slack: https://aihub.qualcomm.com/community/slack

GitHub Issues: https://github.com/qualcomm/ai-hub-models/issues

Email: ai-hub-support@qti.qualcomm.com.

LICENSE

Qualcomm® AI Hub Models is licensed under BSD-3. See the LICENSE file.

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Qualcomm® AI Hub Models

NEW: Quick Start with the AI Hub Models CLI

Setup

1. Install Python Package

2. Configure AI Hub Workbench Access

Getting Started

Export and Run A Model on a Physical Device

End-To-End Model Demos

Sample Applications

Model Support Data

On-Device Runtimes

Device Hardware & Precision

Chipsets

Devices

Model Directory

Computer Vision

Multimodal

Audio

Generative AI

Need help?

LICENSE

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README.md

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Qualcomm® AI Hub Models

NEW: Quick Start with the AI Hub Models CLI

Setup

1. Install Python Package

2. Configure AI Hub Workbench Access

Getting Started

Export and Run A Model on a Physical Device

End-To-End Model Demos

Sample Applications

Model Support Data

On-Device Runtimes

Device Hardware & Precision

Chipsets

Devices

Model Directory

Computer Vision

Multimodal

Audio

Generative AI

Need help?

LICENSE