TensorFlow 1 Detection Model Zoo
We provide a collection of detection models pre-trained on the
COCO dataset, the
Kitti dataset, the
Open Images dataset,
the AVA v2.1 dataset the
iNaturalist Species Detection Dataset
and the
Snapshot Serengeti Dataset.
These models can be useful for out-of-the-box inference if you are interested in
categories already in those datasets. They are also useful for initializing your
models when training on novel datasets.
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
model in the
samples/configs directory,
- a download link to a tar.gz file containing the pre-trained model,
- model speed — we report running time in ms per 600x600 image (including
all 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. Also note that desktop GPU timing
does not always reflect mobile run time. For example Mobilenet V2 is faster
on mobile devices than Mobilenet V1, but is slightly slower on desktop GPU.
- detector performance on subset of the COCO validation set, Open Images test
split, iNaturalist test split, or Snapshot Serengeti LILA.science test
split. as measured by the dataset-specific mAP measure. Here, higher is
better, and we only report bounding box mAP rounded to the nearest integer.
- Output types (
Boxes, and Masks if applicable )
You can un-tar each tar.gz file via, e.g.,:
tar -xzvf ssd_mobilenet_v1_coco.tar.gz
Inside the un-tar’ed directory, you will find:
- a graph proto (
graph.pbtxt)
- a checkpoint (
model.ckpt.data-00000-of-00001, model.ckpt.index,
model.ckpt.meta)
- 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 (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)
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.
- Mobile model only: a TfLite file (
model.tflite) that can be deployed on
mobile devices.
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
v1.12.0 release
version of TensorFlow; this being said, each frozen inference graph can be
regenerated using your current version of TensorFlow by re-running the
exporter,
pointing it at the model directory as well as the corresponding config file
in
samples/configs.
COCO-trained models
Note: The asterisk (☆) at the end of model name indicates that this model
supports TPU training.
Note: If you download the tar.gz file of quantized models and un-tar, you will
get different set of files - a checkpoint, a config file and tflite frozen
graphs (txt/binary).
Mobile models
Pixel4 Edge TPU models
Pixel4 DSP models
Kitti-trained models
Open Images-trained models
iNaturalist Species-trained models
AVA v2.1 trained models
Snapshot Serengeti Camera Trap trained models
Pixel 6 Edge TPU models