Models

From catalog/models.json. The fit column says where each model is expected to run: device:ram>=512k needs that much board RAM, +int8 needs model.quantize: int8, device:psram needs a board with PSRAM, edge and cloud run through westwind serve. The memory plan is the planned activation memory and the size of the ExecuTorch program for each conversion that has been measured.

Model idTaskSourceSizeInputFitMemory planLicense
mediapipe/face_detector/blaze_face_short_rangedetectionmediapipe224 KiB[1, 128, 128, 3] NHWC float32
device:psramedgecloud
fp32: 1.7 MiB, .pte 487 KiB
int8-cortex-m: 1.0 MiB, .pte 454 KiB
Apache-2.0
mediapipe/image_segmenter/selfie_segmentersegmentationmediapipe244 KiB[1, 256, 256, 3] NHWC float32
device:psramedgecloud
fp32: 4.3 MiB, .pte 550 KiB
Apache-2.0
mediapipe/image_segmenter/deeplab_v3segmentationmediapipe2.7 MiBsee model
edgecloud
Apache-2.0
mediapipe/pose_landmarker/pose_landmarker_liteposemediapipe5.5 MiBsee model
edgecloud
The landmark model converts; the detector uses sparse weights the converter does not read yet. Run it with westwind serve (LiteRT).
Apache-2.0
mediapipe/hand_landmarker/hand_landmarkerposemediapipe7.5 MiBsee model
edgecloud
Apache-2.0
mediapipe/face_landmarker/face_landmarkerposemediapipe3.6 MiBsee model
edgecloud
Apache-2.0
mediapipe/audio_classifier/yamnetclassificationmediapipe3.9 MiBsee model
device:psramedgecloud
Apache-2.0
mediapipe/object_detector/efficientdet_lite0detectionmediapipe4.4 MiBsee model
edgecloud
Apache-2.0
builtin/tiny_digit_netclassificationbuiltin[1, 1, 28, 28] NCHW float32
device:ram>=128k
Apache-2.0
builtin/anomaly_netanomalybuiltin[1, 32] NC float32
device:ram>=32k
Apache-2.0
builtin/kws_netkeyword-spottingbuiltin[1, 1, 49, 10] NCHW float32
device:ram>=128k
Apache-2.0
hf-space/eoinedge/sam3segmentationhuggingfacesee model
cloud
see Space