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Commit ad3a9567 authored by Yeqing Li's avatar Yeqing Li Committed by A. Unique TensorFlower
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Updates binary paths in the README file. 

PiperOrigin-RevId: 414057732
parent 063cef34
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official/legacy/detection/README.md
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......@@ -30,7 +30,7 @@ RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
......@@ -41,7 +41,7 @@ python3 ~/models/official/vision/detection/main.py \
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
[detection/](https://github.com/tensorflow/models/tree/master/official/legacy/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
......@@ -56,7 +56,7 @@ MODEL_DIR="<path to the directory to store model files>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
......@@ -87,7 +87,7 @@ following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
......@@ -105,7 +105,7 @@ Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir="${MODEL_DIR?}" \
......@@ -115,7 +115,7 @@ python3 ~/models/official/vision/detection/main.py \
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir="${MODEL_DIR?}" \
......@@ -126,7 +126,7 @@ python3 ~/models/official/vision/detection/main.py \
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
......@@ -160,7 +160,7 @@ RESNET_CHECKPOINT="<path to the pre-trained Resnet-50 checkpoint>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
......@@ -172,7 +172,7 @@ python3 ~/models/official/vision/detection/main.py \
The pre-trained ResNet-50 checkpoint can be downloaded [here](https://storage.cloud.google.com/cloud-tpu-checkpoints/model-garden-vision/detection/resnet50-2018-02-07.tar.gz).
Note: The ResNet implementation under
[detection/](https://github.com/tensorflow/models/tree/master/official/vision/detection)
[detection/](https://github.com/tensorflow/models/tree/master/official/legacy/detection)
is currently different from the one under
[classification/](https://github.com/tensorflow/models/tree/master/official/vision/image_classification),
so the checkpoints are not compatible.
......@@ -187,7 +187,7 @@ MODEL_DIR="<path to the directory to store model files>"
TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu="${TPU_NAME?}" \
--model_dir="${MODEL_DIR?}" \
......@@ -218,7 +218,7 @@ following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
......@@ -240,7 +240,7 @@ Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
......@@ -251,7 +251,7 @@ python3 ~/models/official/vision/detection/main.py \
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
......@@ -263,7 +263,7 @@ python3 ~/models/official/vision/detection/main.py \
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
......@@ -297,7 +297,7 @@ TRAIN_FILE_PATTERN="<path to the TFRecord training data>"
EVAL_FILE_PATTERN="<path to the TFRecord validation data>"
VAL_JSON_FILE="<path to the validation annotation JSON file>"
SHAPE_PRIOR_PATH="<path to shape priors>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
......@@ -337,7 +337,7 @@ following command.
```bash
TPU_NAME="<your GCP TPU name>"
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
......@@ -359,7 +359,7 @@ Multi-GPUs example (assuming there are 8GPU connected to the host):
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=mirrored \
--num_gpus=8 \
--model_dir=${MODEL_DIR} \
......@@ -372,7 +372,7 @@ A single GPU example
```bash
MODEL_DIR="<path to the directory to store model files>"
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--strategy_type=one_device \
--num_gpus=1 \
--model_dir=${MODEL_DIR} \
......@@ -385,7 +385,7 @@ python3 ~/models/official/vision/detection/main.py \
An example with inline configuration (YAML or JSON format):
```
python3 ~/models/official/vision/detection/main.py \
python3 ~/models/official/legacy/detection/main.py \
--model_dir=<model folder> \
--strategy_type=one_device \
--num_gpus=1 \
......@@ -407,7 +407,7 @@ use_tpu: False
### Run the evaluation (after training)
```
python3 /usr/share/models/official/vision/detection/main.py \
python3 /usr/share/models/official/legacy/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
......
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