We provide a collection of detection models pre-trained on the
We provide a collection of detection models pre-trained on the[COCO
[COCO dataset](http://mscoco.org).
dataset](http://mscoco.org) and the [Kitti dataset](http://www.cvlibs.net/datasets/kitti/).
These models can be useful for out-of-the-box inference if you are interested
These models can be useful for
in categories already in COCO (e.g., humans, cars, etc).
out-of-the-box inference if you are interested in categories already in COCO
They are also useful for initializing your models when training on novel
(e.g., humans, cars, etc). They are also useful for initializing your models when
datasets.
training on novel datasets.
In the table below, we list each such pre-trained model including:
In the table below, we list each such pre-trained model including:
* a model name that corresponds to a config file that was used to train this
* a model name that corresponds to a config file that was used to train this
model in the `samples/configs` directory,
model in the `samples/configs` directory,
* a download link to a tar.gz file containing the pre-trained model,
* a download link to a tar.gz file containing the pre-trained model,
* model speed (one of {slow, medium, fast}),
* model speed --- we report running time in ms per 600x600 image (including all
* detector performance on COCO data as measured by the COCO mAP measure.
pre and post-processing), but please be
aware that these timings depend highly on one's specific hardware
configuration (these timings were performed using an Nvidia
GeForce GTX TITAN X card) and should be treated more as relative timings in
many cases.
* detector performance on subset of the COCO validation set.
Here, higher is better, and we only report bounding box mAP rounded to the
Here, higher is better, and we only report bounding box mAP rounded to the
nearest integer.
nearest integer.
* Output types (currently only `Boxes`)
* Output types (currently only `Boxes`)
...
@@ -32,12 +37,54 @@ Inside the un-tar'ed directory, you will find:
...
@@ -32,12 +37,54 @@ Inside the un-tar'ed directory, you will find:
* a frozen graph proto with weights baked into the graph as constants
* a frozen graph proto with weights baked into the graph as constants
(`frozen_inference_graph.pb`) to be used for out of the box inference
(`frozen_inference_graph.pb`) to be used for out of the box inference
(try this out in the Jupyter notebook!)
(try this out in the Jupyter notebook!)
* a config file (`pipeline.config`) which was used to generate the graph. These
directly correspond to a config file in the
[samples/configs](https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs)) directory but often with a modified score threshold. In the case
of the heavier Faster R-CNN models, we also provide a version of the model
that uses a highly reduced number of proposals for speed.
| Model name | Speed | COCO mAP | Outputs |
Some remarks on frozen inference graphs:
* If you try to evaluate the frozen graph, you may find performance numbers for
some of the models to be slightly lower than what we report in the below
tables. This is because we discard detections with scores below a
threshold (typically 0.3) when creating the frozen graph. This corresponds
effectively to picking a point on the precision recall curve of
a detector (and discarding the part past that point), which negatively impacts
standard mAP metrics.
* Our frozen inference graphs are generated using the
