https://github.com/koorosh/poker-vision

https://github.com/koorosh/poker-vision

Last synced: 20 days ago
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• Host: GitHub
• URL: https://github.com/koorosh/poker-vision
• Owner: koorosh
• Created: 2018-11-13T07:42:24.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-11-17T16:12:49.000Z (about 6 years ago)
• Last Synced: 2024-10-27T17:37:23.200Z (2 months ago)
• Language: Python
• Size: 10.7 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# poker-vision

## Setup

- Create empty directories in your project:

```bash

mkdir ./data ./images ./models ./zoo_models

```

- Run docker container

```bash

docker-compose up -d

```

## Prepare data

- Copy images and annotation files to ./images directory

- Convert images to TF_Record format:

```bash

# Run from /models/research folder

python /sh/create_pascal_tf_record.py \

    --label_map_path=/data/poker_label_map.pbtxt \

    --data_dir=/images/POKER20181113 \

    --year=Poker2018 \

    --set=train \

    --output_path=/data/poker_train.record

python /sh/create_pascal_tf_record.py \

    --label_map_path=/data/poker_label_map.pbtxt \

    --data_dir=/images/POKER20181113 \

    --year=Poker2018 \

    --set=val \

    --output_path=/data/poker_val.record

```

## Run model training

- Adjust existing model with checkpoints and config

- Run actual training

```bash

# From the tensorflow/models/research/ directory

PIPELINE_CONFIG_PATH=/models/2018-11-13_ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco.config

MODEL_DIR=/models/2018-11-13_ssdlite_mobilenet_v2

NUM_TRAIN_STEPS=50000

SAMPLE_1_OF_N_EVAL_EXAMPLES=1

python object_detection/model_main.py \

    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \

    --model_dir=${MODEL_DIR} \

    --num_train_steps=${NUM_TRAIN_STEPS} \

    --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \

    --alsologtostderr

```

## Export frozen_model for inference

```bash

# From tensorflow/models/research/

INPUT_TYPE=image_tensor

PIPELINE_CONFIG_PATH={path to pipeline config file}

TRAINED_CKPT_PREFIX={path to model.ckpt}

EXPORT_DIR={path to folder that will be used for export}

python object_detection/export_inference_graph.py \

    --input_type=${INPUT_TYPE} \

    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \

    --trained_checkpoint_prefix=${TRAINED_CKPT_PREFIX} \

    --output_directory=${EXPORT_DIR}

```