resnet50

ResNet 50

The ResNet-50 architecture is a convolutional neural network pre-trained on the ImageNet-1k dataset. Originally introduced by He et al. in the landmark paper, Deep Residual Learning for Image Recognition, this model utilizes residual mapping to overcome the vanishing gradient problem, enabling the training of substantially deeper networks.

Model description

The model was converted from a checkpoint from PyTorch Vision.

The source checkpoint has: acc@1 (on ImageNet-1K): 80.858% acc@5 (on ImageNet-1K): 95.434% num_params: 25,557,032

Available Model Files

Quantization Schema

resnet50_int8_channelwise.tflite was quantized with AI Edge Quantizer's static W8A8 recipe (STATIC_WI8_AI8).

The schema is:

Calibration used real ImageNet validation images with the TorchVision ResNet preprocessing flow. When using APIs that expose raw tensor buffers, prepare the input and output using the quantization parameters stored in the model.

Compatibility

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.

How to Use

​​1. Install Dependencies Ensure your Python environment is set up with the required libraries. Run the following command in your terminal:

pip install numpy Pillow huggingface_hub ai-edge-litert

2. Prepare Your Image The script expects an image file to analyze. Make sure you have an image (e.g., cat.jpg or car.png) saved in the same working directory as your script.

3. Save the Script Create a new file named classify.py, paste the script below into it, and save the file:

#!/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
   s = 232
   if w < h:
       img = img.resize((s, int(h * s / w)), Image.BILINEAR)
   else:
       img = img.resize((int(w * s / h), s), Image.BILINEAR)
   left = int(round((img.size[0] - 224) / 2.0))
   top = int(round((img.size[1] - 224) / 2.0))
   img = img.crop((left, top, left + 224, top + 224))

   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.ascontiguousarray(x.transpose(2, 0, 1)[None])

def main():
   ap = argparse.ArgumentParser()
   ap.add_argument("--image", required=True)
   args = ap.parse_args()

   model_path = hf_hub_download("litert-community/resnet50", "resnet50.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()

4. Execute the Python Script Run the below command:

python classify.py --image cat.jpg

BibTeX entry and citation info

@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778},
year={2016}
}