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Commit 3ad3d806 authored by Gil Raphaelli's avatar Gil Raphaelli
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inception/README.md
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...@@ -67,7 +67,7 @@ download and convert the ImageNet data to native TFRecord format. The TFRecord ...@@ -67,7 +67,7 @@ download and convert the ImageNet data to native TFRecord format. The TFRecord
format consists of a set of sharded files where each entry is a serialized format consists of a set of sharded files where each entry is a serialized
`tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG `tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG
encoded) as well as metadata such as label and bounding box information. See encoded) as well as metadata such as label and bounding box information. See
[`parse_example_proto`](image_processing.py) for details. [`parse_example_proto`](inception/image_processing.py) for details.
We provide a single We provide a single
[script](inception/data/download_and_preprocess_imagenet.sh) [script](inception/data/download_and_preprocess_imagenet.sh)
...@@ -155,7 +155,7 @@ We have tested several hardware setups for training this model from scratch but ...@@ -155,7 +155,7 @@ We have tested several hardware setups for training this model from scratch but
we emphasize that depending your hardware set up, you may need to adapt the we emphasize that depending your hardware set up, you may need to adapt the
batch size and learning rate schedule. batch size and learning rate schedule.
Please see the comments in `inception_train.py` for a few selected learning rate Please see the comments in [`inception_train.py`](inception/inception_train.py) for a few selected learning rate
plans based on some selected hardware setups. plans based on some selected hardware setups.
To train this model, you simply need to specify the following: To train this model, you simply need to specify the following:
...@@ -281,7 +281,7 @@ prediction from the model matched the ImageNet label -- in this case, 73.5%. ...@@ -281,7 +281,7 @@ prediction from the model matched the ImageNet label -- in this case, 73.5%.
If you wish to run the eval just once and not periodically, append the If you wish to run the eval just once and not periodically, append the
`--run_once` option. `--run_once` option.
Much like the training script, `imagenet_eval.py` also Much like the training script, [`imagenet_eval.py`](inception/imagenet_eval.py) also
exports summaries that may be visualized in TensorBoard. These summaries exports summaries that may be visualized in TensorBoard. These summaries
calculate additional statistics on the predictions (e.g. recall @ 5) as well calculate additional statistics on the predictions (e.g. recall @ 5) as well
as monitor the statistics of the model activations and weights during as monitor the statistics of the model activations and weights during
...@@ -303,7 +303,7 @@ There is a single automated script that downloads the data set and converts ...@@ -303,7 +303,7 @@ There is a single automated script that downloads the data set and converts
it to the TFRecord format. Much like the ImageNet data set, each record in the it to the TFRecord format. Much like the ImageNet data set, each record in the
TFRecord format is a serialized `tf.Example` proto whose entries include TFRecord format is a serialized `tf.Example` proto whose entries include
a JPEG-encoded string and an integer label. Please see a JPEG-encoded string and an integer label. Please see
[`parse_example_proto`](image_processing.py) for details. [`parse_example_proto`](inception/image_processing.py) for details.
The script just takes a few minutes to run depending your network connection The script just takes a few minutes to run depending your network connection
speed for downloading and processing the images. Your hard disk requires 200MB speed for downloading and processing the images. Your hard disk requires 200MB
...@@ -333,10 +333,10 @@ files in the `DATA_DIR`. The files will match the patterns ...@@ -333,10 +333,10 @@ files in the `DATA_DIR`. The files will match the patterns
`train-????-of-00001` and `validation-?????-of-00001`, respectively. `train-????-of-00001` and `validation-?????-of-00001`, respectively.
**NOTE** If you wish to prepare a custom image data set for transfer learning, **NOTE** If you wish to prepare a custom image data set for transfer learning,
you will need to invoke [`build_image_data.py`](data/build_image_data.py) you will need to invoke [`build_image_data.py`](inception/data/build_image_data.py)
on your custom data set. on your custom data set.
Please see the associated options and assumptions behind this script by reading Please see the associated options and assumptions behind this script by reading
the comments section of [`build_image_data.py`](data/build_image_data.py). the comments section of [`build_image_data.py`](inception/data/build_image_data.py).
The second piece you will need is a trained Inception v3 image model. You have The second piece you will need is a trained Inception v3 image model. You have
the option of either training one yourself (See the option of either training one yourself (See
...@@ -390,7 +390,7 @@ if you wish to continue training a pre-trained model from a checkpoint. If you ...@@ -390,7 +390,7 @@ if you wish to continue training a pre-trained model from a checkpoint. If you
set this flag to true, you can train a new classification layer from scratch. set this flag to true, you can train a new classification layer from scratch.
In order to understand how `--fine_tune` works, please see the discussion In order to understand how `--fine_tune` works, please see the discussion
on `Variables` in the TensorFlow-Slim [`README.md`](slim/README.md). on `Variables` in the TensorFlow-Slim [`README.md`](inception/slim/README.md).
Putting this all together you can retrain a pre-trained Inception-v3 model Putting this all together you can retrain a pre-trained Inception-v3 model
on the flowers data set with the following command. on the flowers data set with the following command.
...@@ -472,7 +472,7 @@ Succesfully loaded model from /tmp/flowers/model.ckpt-1999 at step=1999. ...@@ -472,7 +472,7 @@ Succesfully loaded model from /tmp/flowers/model.ckpt-1999 at step=1999.
One can use the existing scripts supplied with this model to build a new One can use the existing scripts supplied with this model to build a new
dataset for training or fine-tuning. The main script to employ is dataset for training or fine-tuning. The main script to employ is
[`build_image_data.py`](./build_image_data.py). Briefly, [`build_image_data.py`](inception/data/build_image_data.py). Briefly,
this script takes a structured this script takes a structured
directory of images and converts it to a sharded `TFRecord` that can be read directory of images and converts it to a sharded `TFRecord` that can be read
by the Inception model. by the Inception model.
...@@ -503,12 +503,12 @@ unique label for the images that reside within that sub-directory. The images ...@@ -503,12 +503,12 @@ unique label for the images that reside within that sub-directory. The images
may be JPEG or PNG images. We do not support other images types currently. may be JPEG or PNG images. We do not support other images types currently.
Once the data is arranged in this directory structure, we can run Once the data is arranged in this directory structure, we can run
`build_image_data.py` on the data to generate the sharded `TFRecord` dataset. [`build_image_data.py`](inception/data/build_image_data.py) on the data to generate the sharded `TFRecord` dataset.
Each entry of the `TFRecord` is a serialized `tf.Example` protocol buffer. Each entry of the `TFRecord` is a serialized `tf.Example` protocol buffer.
A complete list of information contained in the `tf.Example` is described A complete list of information contained in the `tf.Example` is described
in the comments of `build_image_data.py`. in the comments of [`build_image_data.py`](inception/data/build_image_data.py).
To run `build_image_data.py`, you can run the following command line: To run [`build_image_data.py`](inception/data/build_image_data.py), you can run the following command line:
```shell ```shell
# location to where to save the TFRecord data. # location to where to save the TFRecord data.
...@@ -578,7 +578,7 @@ some general considerations for novices. ...@@ -578,7 +578,7 @@ some general considerations for novices.
Roughly 5-10 hyper-parameters govern the speed at which a network is trained. Roughly 5-10 hyper-parameters govern the speed at which a network is trained.
In addition to `--batch_size` and `--num_gpus`, there are several constants In addition to `--batch_size` and `--num_gpus`, there are several constants
defined in [inception_train.py](./inception_train.py) which dictate the defined in [inception_train.py](inception/inception_train.py) which dictate the
learning schedule. learning schedule.
```shell ```shell
...@@ -652,7 +652,7 @@ model architecture, this corresponds to 16GB of CPU memory. You may lower ...@@ -652,7 +652,7 @@ model architecture, this corresponds to 16GB of CPU memory. You may lower
`input_queue_memory_factor` in order to decrease the memory footprint. Keep `input_queue_memory_factor` in order to decrease the memory footprint. Keep
in mind though that lowering this value drastically may result in a model in mind though that lowering this value drastically may result in a model
with slightly lower predictive accuracy when training from scratch. Please with slightly lower predictive accuracy when training from scratch. Please
see comments in [`image_processing.py`](./image_processing.py) for more details. see comments in [`image_processing.py`](inception/image_processing.py) for more details.
## Troubleshooting ## Troubleshooting
...@@ -693,7 +693,7 @@ the entire model architecture. ...@@ -693,7 +693,7 @@ the entire model architecture.
We targeted a desktop with 128GB of CPU ram connected to 8 NVIDIA Tesla K40 We targeted a desktop with 128GB of CPU ram connected to 8 NVIDIA Tesla K40
GPU cards but we have run this on desktops with 32GB of CPU ram and 1 NVIDIA GPU cards but we have run this on desktops with 32GB of CPU ram and 1 NVIDIA
Tesla K40. You can get a sense of the various training configurations we Tesla K40. You can get a sense of the various training configurations we
tested by reading the comments in [`inception_train.py`](./inception_train.py). tested by reading the comments in [`inception_train.py`](inception/inception_train.py).
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