You can deploy this model and start earning money today!

ketiswp/
mediapipe-EfficientNet-Lite0-ImageNet-224-fp32-onnx

Name: mediapipe-EfficientNet-Lite0-ImageNet-224-fp32-onnx
Availability: InStock

Views

No views yet

MediaPipe EfficientNet-Lite0 ImageNet 224 FP32 ONNX

FP32 ONNX version of MediaPipe EfficientNet-Lite0 ImageNet 224 for image classification.

Model Files

File	Purpose	Format
`model.onnx`	Downloadable converted model	ONNX
`source/model.tflite`	Original model	TensorFlow Lite
`graphs/netron.png`	ONNX graph visualization	PNG
`mlir/onnx.mlir`	ONNX-MLIR import result	MLIR text

Parameter Summary

Item	Value
Precision	FP32
ONNX file size	17.74 MiB
Initializer tensors	103
Stored initializer elements	4,631,004
External weight files	None
Original format	TensorFlow Lite

Stored initializer elements includes weights, biases, quantization scales, zero-points, and other constant tensors. It is not a trainable-parameter count.

Original Model Inference

pip install huggingface_hub numpy ai-edge-litert

python

import numpy as np
from ai_edge_litert.interpreter import Interpreter
from huggingface_hub import hf_hub_download

repo_id = "ketiswp/mediapipe-EfficientNet-Lite0-ImageNet-224-fp32-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="source/model.tflite")
interpreter = Interpreter(model_path=model_path, num_threads=1)

for item in interpreter.get_input_details():
    signature = [int(value) for value in item.get("shape_signature", item["shape"])]
    shape = [value if value > 0 else 1 for value in signature]
    if shape != [int(value) for value in item["shape"]]:
        interpreter.resize_tensor_input(int(item["index"]), shape, strict=False)

interpreter.allocate_tensors()
for item in interpreter.get_input_details():
    value = np.zeros(tuple(int(dim) for dim in item["shape"]), dtype=item["dtype"])
    interpreter.set_tensor(int(item["index"]), value)

interpreter.invoke()
outputs = [interpreter.get_tensor(int(item["index"]))
           for item in interpreter.get_output_details()]
print([(value.shape, str(value.dtype)) for value in outputs])

Converted ONNX Inference

pip install huggingface_hub numpy onnxruntime

python

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download

repo_id = "ketiswp/mediapipe-EfficientNet-Lite0-ImageNet-224-fp32-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="model.onnx")

options = ort.SessionOptions()
options.intra_op_num_threads = 1
options.inter_op_num_threads = 1
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
session = ort.InferenceSession(
    model_path,
    sess_options=options,
    providers=["CPUExecutionProvider"],
)

dtype_by_ort_type = {
    "tensor(float)": np.float32,
    "tensor(double)": np.float64,
    "tensor(float16)": np.float16,
    "tensor(int64)": np.int64,
    "tensor(int32)": np.int32,
    "tensor(int16)": np.int16,
    "tensor(int8)": np.int8,
    "tensor(uint8)": np.uint8,
    "tensor(bool)": np.bool_,
}
feeds = {}
for item in session.get_inputs():
    shape = [dim if isinstance(dim, int) and dim > 0 else 1 for dim in item.shape]
    feeds[item.name] = np.zeros(shape, dtype=dtype_by_ort_type[item.type])

outputs = session.run(None, feeds)
print([(item.name, value.shape, str(value.dtype))
       for item, value in zip(session.get_outputs(), outputs)])

Paired Model

INT8/UINT8 version

Source

Project Validation

FP32/quantized comparison, conversion results, and reproduction code

This repository also includes the Netron graph, ONNX Dialect MLIR, and static MLIR dependency graph for this model variant.