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@hgaiser
hgaiser authored May 12, 2018
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Expand Up @@ -20,7 +20,7 @@ In general, inference of the network works as follows:
boxes, scores, labels = model.predict_on_batch(inputs)
```

Where `boxes` are shaped `(None, None, 4)` (for `(x1, y1, x2, y2)`), scores is shaped `(None, None)` (classification score) and labels is shaped `(None, None)` (label corresponding to the score).
Where `boxes` are shaped `(None, None, 4)` (for `(x1, y1, x2, y2)`), scores is shaped `(None, None)` (classification score) and labels is shaped `(None, None)` (label corresponding to the score). In all three outputs, the first dimension represents the shape and the second dimension indexes the list of detections.

Loading models can be done in the following manner:
```python
Expand All @@ -31,7 +31,7 @@ model = load_model('/path/to/model.h5', backbone_name='resnet50')
Execution time on NVIDIA Pascal Titan X is roughly 75msec for an image of shape `1000x800x3`.

### Converting a training model to inference model
The training procedure of `keras-retinanet` works with training models. These are stripped down versions compared to the inference model and only contains the layers necessary for training (regression and classification values). If you wish to do inference on a model (perform object detection on an image), you need to convert the trained model to an inference model. This is done as follows:
The training procedure of `keras-retinanet` works with *training models*. These are stripped down versions compared to the *inference model* and only contains the layers necessary for training (regression and classification values). If you wish to do inference on a model (perform object detection on an image), you need to convert the trained model to an inference model. This is done as follows:

```shell
# Running directly from the repository:
Expand All @@ -54,8 +54,8 @@ If you installed `keras-retinanet` correctly, the train script will be installed
However, if you make local modifications to the `keras-retinanet` repository, you should run the script directly from the repository.
That will ensure that your local changes will be used by the train script.

The default backbone is 'resnet50'. You can change this using the '--backbone=xxx' argument in the running script.
xxx can be one of the backbones in resnet models (resnet50, resnet101, resnet152), mobilenet models (mobilenet128_1.0, mobilenet128_0.75, mobilenet160_1.0, etc), densenet models or vgg models. The different options are defined by each model in their corresponding python scripts (resnet.py, mobilenet.py, etc).
The default backbone is `resnet50`. You can change this using the `--backbone=xxx` argument in the running script.
`xxx` can be one of the backbones in resnet models (`resnet50`, `resnet101`, `resnet152`), mobilenet models (`mobilenet128_1.0`, `mobilenet128_0.75`, `mobilenet160_1.0`, etc), densenet models or vgg models. The different options are defined by each model in their corresponding python scripts (`resnet.py`, `mobilenet.py`, etc).

Trained models can't be used directly for inference. To convert a trained model to an inference model, check [here](https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model).

Expand All @@ -78,21 +78,21 @@ keras_retinanet/bin/train.py coco /path/to/MS/COCO
retinanet-train coco /path/to/MS/COCO
```

The pretrained MS COCO model can be downloaded [here](https://github.com/fizyr/keras-retinanet/releases/download/0.2/resnet50_coco_best_v2.0.3.h5). Results using the `cocoapi` are shown below (note: according to the paper, this configuration should achieve a mAP of 0.357).
The pretrained MS COCO model can be downloaded [here](https://github.com/fizyr/keras-retinanet/releases/download/0.2/resnet50_coco_best_v2.1.0.h5). Results using the `cocoapi` are shown below (note: according to the paper, this configuration should achieve a mAP of 0.357).

```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.533
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.301
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.529
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.364
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.565
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.666
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.536
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.373
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.384
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.470
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.307
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.491
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.533
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.577
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678
```

For training on [OID](https://github.com/openimages/dataset), run:
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