Inception_v3
Inception v3 model pre-trained on ImageNet-1k. It was introduced in Rethinking the Inception Architecture for Computer Vision by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna.
Intended uses & limitations
The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
Model description
The model was converted from a checkpoint from PyTorch Vision Inception_V3_Weights.IMAGENET1K_V1.
The original model has:
acc@1 (on ImageNet-1K): 77.294%
acc@5 (on ImageNet-1K): 93.450%
num_params: 27,161,264
Use
#!/usr/bin/env python3
import argparse, json
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download
from ai_edge_litert.compiled_model import CompiledModel
def preprocess(img: Image.Image) -> np.ndarray:
img = img.convert("RGB")
w, h = img.size
# Inception_v3 expects a resize to 342 prior to the 299 central crop
s = 342
if w < h:
img = img.resize((s, int(round(h * s / w))), Image.BILINEAR)
else:
img = img.resize((int(round(w * s / h)), s), Image.BILINEAR)
# Central crop to 299x299
left = (img.size[0] - 299) // 2
top = (img.size[1] - 299) // 2
img = img.crop((left, top, left + 299, top + 299))
# Rescale to [0.0, 1.0] and Normalize
x = np.asarray(img, dtype=np.float32) / 255.0
x = (x - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array(
[0.229, 0.224, 0.225], dtype=np.float32
)
return np.transpose(x, (2, 0, 1))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--image", required=True)
args = ap.parse_args()
# Download the TFLite model and labels
model_path = hf_hub_download("litert-community/inception_v3", "inception_v3.tflite")
labels_path = hf_hub_download(
"huggingface/label-files", "imagenet-1k-id2label.json", repo_type="dataset"
)
with open(labels_path, "r", encoding="utf-8") as f:
id2label = {int(k): v for k, v in json.load(f).items()}
img = Image.open(args.image)
x = preprocess(img)
model = CompiledModel.from_file(model_path)
inp = model.create_input_buffers(0)
out = model.create_output_buffers(0)
inp[0].write(x)
model.run_by_index(0, inp, out)
req = model.get_output_buffer_requirements(0, 0)
y = out[0].read(req["buffer_size"] // np.dtype(np.float32).itemsize, np.float32)
pred = int(np.argmax(y))
label = id2label.get(pred, f"class_{pred}")
print(f"Top-1 class index: {pred}")
print(f"Top-1 label: {label}")
if __name__ == "__main__":
main()
BibTeX entry and citation info
@inproceedings{szegedy2016rethinking,
title={Rethinking the inception architecture for computer vision},
author={Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2818--2826},
year={2016}
}
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Dataset used to train litert-community/inception_v3
Paper for litert-community/inception_v3
Evaluation results
- Top 1 Accuracy (Full Precision) on ImageNet-1kvalidation set self-reported0.773
- Top 5 Accuracy (Full Precision) on ImageNet-1kvalidation set self-reported0.934