tensorflow / tensorflow/models

Unable to find checkpoint for object detection training using tf2/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.config

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@laxmareddyp is already working on this.

Since Jun 2, 2023.

models:research type:bug
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Description

Prerequisites

Please answer the following questions for yourself before submitting an issue.

  • [Yes ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
  • [ Yes] I am reporting the issue to the correct repository. (Model Garden official or research directory)
  • [Yes ] I checked to make sure that this issue has not been filed already.

1. The entire URL of the file you are using

https://github.com/tensorflow/models/tree/master/official/...

2. Describe the bug

I am trying to do custom object detection training using ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8 and I downloaded the checkpoint .gz file from the model zoo. But when I checked that folder the files are as below-
image

But in the ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.config file, the name of the checkpoint is different. I am not seeing any file name as mobilenet_v2.ckpt-1 in downloaded checkpoints-
image

Please tell me what should be the correct value in this line as per downloaded files from the model zoo-
image

3. Steps to reproduce

!python object_detection/model_main_tf2.py
--pipeline_config_path={PIPELINE_CONFIG_PATH}
--model_dir={MODEL_DIR}
--alsologtostderr

4. Expected behavior

Training should be started without any issue.

5. Additional context

Include any logs that would be helpful to diagnose the problem.

6. System information

  • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Colab
  • Mobile device name if the issue happens on a mobile device:
  • TensorFlow installed from (source or binary): pip install
  • TensorFlow version (use command below): 2.12.0
  • Python version: 3.10.11
  • Bazel version (if compiling from source):
  • GCC/Compiler version (if compiling from source):
  • CUDA/cuDNN version:
  • GPU model and memory: T4

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  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Assessment

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