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Commit 8f5f819f authored by Kaushik Shivakumar's avatar Kaushik Shivakumar
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Merge branch 'master' of https://github.com/tensorflow/models into latest

parents 7c062a56 709a6617
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research/object_detection/colab_tutorials/inference_tf2_colab.ipynb
View file @ 8f5f819f
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "inference_tf2_colab.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
......@@ -37,24 +24,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LBZ9VWZZFUCT",
"colab": {},
"colab_type": "code",
"colab": {}
"id": "LBZ9VWZZFUCT"
},
"outputs": [],
"source": [
"!pip install -U --pre tensorflow==\"2.2.0\""
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oi28cqGGFWnY",
"colab": {},
"colab_type": "code",
"colab": {}
"id": "oi28cqGGFWnY"
},
"outputs": [],
"source": [
"import os\n",
"import pathlib\n",
......@@ -65,17 +54,17 @@
" os.chdir('..')\n",
"elif not pathlib.Path('models').exists():\n",
" !git clone --depth 1 https://github.com/tensorflow/models"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NwdsBdGhFanc",
"colab": {},
"colab_type": "code",
"colab": {}
"id": "NwdsBdGhFanc"
},
"outputs": [],
"source": [
"# Install the Object Detection API\n",
"%%bash\n",
......@@ -83,17 +72,17 @@
"protoc object_detection/protos/*.proto --python_out=.\n",
"cp object_detection/packages/tf2/setup.py .\n",
"python -m pip install ."
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "yn5_uV1HLvaz",
"colab": {}
"id": "yn5_uV1HLvaz"
},
"outputs": [],
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
......@@ -112,9 +101,7 @@
"from object_detection.builders import model_builder\n",
"\n",
"%matplotlib inline"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -128,11 +115,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-y9R0Xllefec",
"colab": {}
"id": "-y9R0Xllefec"
},
"outputs": [],
"source": [
"def load_image_into_numpy_array(path):\n",
" \"\"\"Load an image from file into a numpy array.\n",
......@@ -167,26 +156,24 @@
" for edge in kp_list:\n",
" tuple_list.append((edge.start, edge.end))\n",
" return tuple_list"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R4YjnOjME1gy",
"colab": {},
"colab_type": "code",
"colab": {}
"id": "R4YjnOjME1gy"
},
"outputs": [],
"source": [
"# @title Choose the model to use, then evaluate the cell.\n",
"MODELS = {'centernet_with_keypoints': 'centernet_hg104_512x512_kpts_coco17_tpu-32', 'centernet_without_keypoints': 'centernet_hg104_512x512_coco17_tpu-8'}\n",
"\n",
"model_display_name = 'centernet_with_keypoints' # @param ['centernet_with_keypoints', 'centernet_without_keypoints']\n",
"model_name = MODELS[model_display_name]"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -202,33 +189,35 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ctPavqlyPuU_",
"colab": {},
"colab_type": "code",
"colab": {}
"id": "ctPavqlyPuU_"
},
"outputs": [],
"source": [
"# Download the checkpoint and put it into models/research/object_detection/test_data/\n",
"\n",
"if model_display_name == 'centernet_with_keypoints':\n",
" !wget http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_512x512_kpts_coco17_tpu-32.tar.gz\n",
" !wget http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_512x512_kpts_coco17_tpu-32.tar.gz\n",
" !tar -xf centernet_hg104_512x512_kpts_coco17_tpu-32.tar.gz\n",
" !mv centernet_hg104_512x512_kpts_coco17_tpu-32/checkpoint models/research/object_detection/test_data/\n",
"else:\n",
" !wget http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_512x512_coco17_tpu-8.tar.gz\n",
" !wget http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_512x512_coco17_tpu-8.tar.gz\n",
" !tar -xf centernet_hg104_512x512_coco17_tpu-8.tar.gz\n",
" !mv centernet_hg104_512x512_coco17_tpu-8/checkpoint models/research/object_detection/test_data/"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4cni4SSocvP_",
"colab": {}
"id": "4cni4SSocvP_"
},
"outputs": [],
"source": [
"pipeline_config = os.path.join('models/research/object_detection/configs/tf2/',\n",
" model_name + '.config')\n",
......@@ -261,9 +250,7 @@
" return detect_fn\n",
"\n",
"detect_fn = get_model_detection_function(detection_model)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -279,11 +266,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5mucYUS6exUJ",
"colab": {}
"id": "5mucYUS6exUJ"
},
"outputs": [],
"source": [
"label_map_path = configs['eval_input_config'].label_map_path\n",
"label_map = label_map_util.load_labelmap(label_map_path)\n",
......@@ -293,9 +282,7 @@
" use_display_name=True)\n",
"category_index = label_map_util.create_category_index(categories)\n",
"label_map_dict = label_map_util.get_label_map_dict(label_map, use_display_name=True)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -321,11 +308,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vr_Fux-gfaG9",
"colab": {}
"id": "vr_Fux-gfaG9"
},
"outputs": [],
"source": [
"image_dir = 'models/research/object_detection/test_images/'\n",
"image_path = os.path.join(image_dir, 'image2.jpg')\n",
......@@ -369,9 +358,7 @@
"plt.figure(figsize=(12,16))\n",
"plt.imshow(image_np_with_detections)\n",
"plt.show()"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -389,11 +376,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "xBgYgSGMhHVi",
"colab": {}
"id": "xBgYgSGMhHVi"
},
"outputs": [],
"source": [
"if detection_model.__class__.__name__ != 'CenterNetMetaArch':\n",
" raise AssertionError('The meta-architecture for this section '\n",
......@@ -462,9 +451,20 @@
"plt.imshow(resized_heatmap_unpadded, alpha=0.7,vmin=0, vmax=160, cmap='viridis')\n",
"plt.title('Object center heatmap (class: ' + class_name + ')')\n",
"plt.show()"
],
"execution_count": null,
"outputs": []
]
}
]
}
\ No newline at end of file
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "inference_tf2_colab.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
research/object_detection/g3doc/tf2_detection_zoo.md
View file @ 8f5f819f
......@@ -23,43 +23,43 @@ model configs in this [directory](../configs/tf2) (also in the linked
Model name | Speed (ms) | COCO mAP | Outputs
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------: | :----------: | :-----:
[CenterNet HourGlass104 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_512x512_coco17_tpu-8.tar.gz) | 70 | 41.6 | Boxes
[CenterNet HourGlass104 Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_512x512_kpts_coco17_tpu-32.tar.gz) | 76 | 40.0/61.4 | Boxes/Keypoints
[CenterNet HourGlass104 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz) | 197 | 43.5 | Boxes
[CenterNet HourGlass104 Keypoints 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_hg104_1024x1024_kpts_coco17_tpu-32.tar.gz) | 211 | 42.8/64.5 | Boxes/Keypoints
[CenterNet Resnet50 V1 FPN 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_resnet50_v1_fpn_512x512_coco17_tpu-8.tar.gz) | 27 | 31.2 | Boxes
[CenterNet Resnet50 V1 FPN Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_resnet50_v1_fpn_512x512_kpts_coco17_tpu-8.tar.gz) | 30 | 29.3/50.7 | Boxes/Keypoints
[CenterNet Resnet101 V1 FPN 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_resnet101_v1_fpn_512x512_coco17_tpu-8.tar.gz) | 34 | 34.2 | Boxes
[CenterNet Resnet50 V2 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_resnet50_v2_512x512_coco17_tpu-8.tar.gz) | 27 | 29.5 | Boxes
[CenterNet Resnet50 V2 Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/centernet_resnet50_v2_512x512_kpts_coco17_tpu-8.tar.gz) | 30 | 27.6/48.2 | Boxes/Keypoints
[EfficientDet D0 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d0_coco17_tpu-32.tar.gz) | 39 | 33.6 | Boxes
[EfficientDet D1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d1_coco17_tpu-32.tar.gz) | 54 | 38.4 | Boxes
[EfficientDet D2 768x768](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d2_coco17_tpu-32.tar.gz) | 67 | 41.8 | Boxes
[EfficientDet D3 896x896](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d3_coco17_tpu-32.tar.gz) | 95 | 45.4 | Boxes
[EfficientDet D4 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d4_coco17_tpu-32.tar.gz) | 133 | 48.5 | Boxes
[EfficientDet D5 1280x1280](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d5_coco17_tpu-32.tar.gz) | 222 | 49.7 | Boxes
[EfficientDet D6 1280x1280](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d6_coco17_tpu-32.tar.gz) | 268 | 50.5 | Boxes
[EfficientDet D7 1536x1536](http://download.tensorflow.org/models/object_detection/tf2/20200710/efficientdet_d7_coco17_tpu-32.tar.gz) | 325 | 51.2 | Boxes
[SSD MobileNet v2 320x320](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz) |19 | 20.2 | Boxes
[SSD MobileNet V1 FPN 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 48 | 29.1 | Boxes
[SSD MobileNet V2 FPNLite 320x320](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz) | 22 | 22.2 | Boxes
[SSD MobileNet V2 FPNLite 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz) | 39 | 28.2 | Boxes
[SSD ResNet50 V1 FPN 640x640 (RetinaNet50)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 46 | 34.3 | Boxes
[SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet50_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 87 | 38.3 | Boxes
[SSD ResNet101 V1 FPN 640x640 (RetinaNet101)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 57 | 35.6 | Boxes
[SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet101_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 104 | 39.5 | Boxes
[SSD ResNet152 V1 FPN 640x640 (RetinaNet152)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet152_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 80 | 35.4 | Boxes
[SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)](http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet152_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 111 | 39.6 | Boxes
[Faster R-CNN ResNet50 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet50_v1_640x640_coco17_tpu-8.tar.gz) | 53 | 29.3 | Boxes
[Faster R-CNN ResNet50 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet50_v1_1024x1024_coco17_tpu-8.tar.gz) | 65 | 31.0 | Boxes
[Faster R-CNN ResNet50 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet50_v1_800x1333_coco17_gpu-8.tar.gz) | 65 | 31.6 | Boxes
[Faster R-CNN ResNet101 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet101_v1_640x640_coco17_tpu-8.tar.gz) | 55 | 31.8 | Boxes
[Faster R-CNN ResNet101 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet101_v1_1024x1024_coco17_tpu-8.tar.gz) | 72 | 37.1 | Boxes
[Faster R-CNN ResNet101 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet101_v1_800x1333_coco17_gpu-8.tar.gz) | 77 | 36.6 | Boxes
[Faster R-CNN ResNet152 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet152_v1_640x640_coco17_tpu-8.tar.gz) | 64 | 32.4 | Boxes
[Faster R-CNN ResNet152 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet152_v1_1024x1024_coco17_tpu-8.tar.gz) | 85 | 37.6 | Boxes
[Faster R-CNN ResNet152 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8.tar.gz) | 101 | 37.4 | Boxes
[Faster R-CNN Inception ResNet V2 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_inception_resnet_v2_640x640_coco17_tpu-8.tar.gz) | 206 | 37.7 | Boxes
[Faster R-CNN Inception ResNet V2 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz) | 236 | 38.7 | Boxes
[Mask R-CNN Inception ResNet V2 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200710/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.tar.gz) | 301 | 39.0/34.6 | Boxes/Masks
[ExtremeNet](http://download.tensorflow.org/models/object_detection/tf2/20200710/extremenet.tar.gz) | -- | -- | Boxes
[CenterNet HourGlass104 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_512x512_coco17_tpu-8.tar.gz) | 70 | 41.6 | Boxes
[CenterNet HourGlass104 Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_512x512_kpts_coco17_tpu-32.tar.gz) | 76 | 40.0/61.4 | Boxes/Keypoints
[CenterNet HourGlass104 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz) | 197 | 43.5 | Boxes
[CenterNet HourGlass104 Keypoints 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_kpts_coco17_tpu-32.tar.gz) | 211 | 42.8/64.5 | Boxes/Keypoints
[CenterNet Resnet50 V1 FPN 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet50_v1_fpn_512x512_coco17_tpu-8.tar.gz) | 27 | 31.2 | Boxes
[CenterNet Resnet50 V1 FPN Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet50_v1_fpn_512x512_kpts_coco17_tpu-8.tar.gz) | 30 | 29.3/50.7 | Boxes/Keypoints
[CenterNet Resnet101 V1 FPN 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet101_v1_fpn_512x512_coco17_tpu-8.tar.gz) | 34 | 34.2 | Boxes
[CenterNet Resnet50 V2 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet50_v2_512x512_coco17_tpu-8.tar.gz) | 27 | 29.5 | Boxes
[CenterNet Resnet50 V2 Keypoints 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet50_v2_512x512_kpts_coco17_tpu-8.tar.gz) | 30 | 27.6/48.2 | Boxes/Keypoints
[EfficientDet D0 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d0_coco17_tpu-32.tar.gz) | 39 | 33.6 | Boxes
[EfficientDet D1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d1_coco17_tpu-32.tar.gz) | 54 | 38.4 | Boxes
[EfficientDet D2 768x768](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d2_coco17_tpu-32.tar.gz) | 67 | 41.8 | Boxes
[EfficientDet D3 896x896](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d3_coco17_tpu-32.tar.gz) | 95 | 45.4 | Boxes
[EfficientDet D4 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d4_coco17_tpu-32.tar.gz) | 133 | 48.5 | Boxes
[EfficientDet D5 1280x1280](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d5_coco17_tpu-32.tar.gz) | 222 | 49.7 | Boxes
[EfficientDet D6 1280x1280](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d6_coco17_tpu-32.tar.gz) | 268 | 50.5 | Boxes
[EfficientDet D7 1536x1536](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d7_coco17_tpu-32.tar.gz) | 325 | 51.2 | Boxes
[SSD MobileNet v2 320x320](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz) |19 | 20.2 | Boxes
[SSD MobileNet V1 FPN 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 48 | 29.1 | Boxes
[SSD MobileNet V2 FPNLite 320x320](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz) | 22 | 22.2 | Boxes
[SSD MobileNet V2 FPNLite 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz) | 39 | 28.2 | Boxes
[SSD ResNet50 V1 FPN 640x640 (RetinaNet50)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 46 | 34.3 | Boxes
[SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet50_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 87 | 38.3 | Boxes
[SSD ResNet101 V1 FPN 640x640 (RetinaNet101)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 57 | 35.6 | Boxes
[SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 104 | 39.5 | Boxes
[SSD ResNet152 V1 FPN 640x640 (RetinaNet152)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet152_v1_fpn_640x640_coco17_tpu-8.tar.gz) | 80 | 35.4 | Boxes
[SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet152_v1_fpn_1024x1024_coco17_tpu-8.tar.gz) | 111 | 39.6 | Boxes
[Faster R-CNN ResNet50 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet50_v1_640x640_coco17_tpu-8.tar.gz) | 53 | 29.3 | Boxes
[Faster R-CNN ResNet50 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet50_v1_1024x1024_coco17_tpu-8.tar.gz) | 65 | 31.0 | Boxes
[Faster R-CNN ResNet50 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet50_v1_800x1333_coco17_gpu-8.tar.gz) | 65 | 31.6 | Boxes
[Faster R-CNN ResNet101 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet101_v1_640x640_coco17_tpu-8.tar.gz) | 55 | 31.8 | Boxes
[Faster R-CNN ResNet101 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet101_v1_1024x1024_coco17_tpu-8.tar.gz) | 72 | 37.1 | Boxes
[Faster R-CNN ResNet101 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet101_v1_800x1333_coco17_gpu-8.tar.gz) | 77 | 36.6 | Boxes
[Faster R-CNN ResNet152 V1 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet152_v1_640x640_coco17_tpu-8.tar.gz) | 64 | 32.4 | Boxes
[Faster R-CNN ResNet152 V1 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet152_v1_1024x1024_coco17_tpu-8.tar.gz) | 85 | 37.6 | Boxes
[Faster R-CNN ResNet152 V1 800x1333](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8.tar.gz) | 101 | 37.4 | Boxes
[Faster R-CNN Inception ResNet V2 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_inception_resnet_v2_640x640_coco17_tpu-8.tar.gz) | 206 | 37.7 | Boxes
[Faster R-CNN Inception ResNet V2 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz) | 236 | 38.7 | Boxes
[Mask R-CNN Inception ResNet V2 1024x1024](http://download.tensorflow.org/models/object_detection/tf2/20200711/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.tar.gz) | 301 | 39.0/34.6 | Boxes/Masks
[ExtremeNet](http://download.tensorflow.org/models/object_detection/tf2/20200711/extremenet.tar.gz) | -- | -- | Boxes
research/object_detection/model_lib_v2.py
View file @ 8f5f819f
......@@ -353,11 +353,18 @@ def load_fine_tune_checkpoint(
labels)
strategy = tf.compat.v2.distribute.get_strategy()
strategy.experimental_run_v2(
_dummy_computation_fn, args=(
features,
labels,
))
if hasattr(tf.distribute.Strategy, 'run'):
strategy.run(
_dummy_computation_fn, args=(
features,
labels,
))
else:
strategy.experimental_run_v2(
_dummy_computation_fn, args=(
features,
labels,
))
restore_from_objects_dict = model.restore_from_objects(
fine_tune_checkpoint_type=checkpoint_type)
......@@ -579,8 +586,12 @@ def train_loop(
def _sample_and_train(strategy, train_step_fn, data_iterator):
features, labels = data_iterator.next()
per_replica_losses = strategy.experimental_run_v2(
train_step_fn, args=(features, labels))
if hasattr(tf.distribute.Strategy, 'run'):
per_replica_losses = strategy.run(
train_step_fn, args=(features, labels))
else:
per_replica_losses = strategy.experimental_run_v2(
train_step_fn, args=(features, labels))
# TODO(anjalisridhar): explore if it is safe to remove the
## num_replicas scaling of the loss and switch this to a ReduceOp.Mean
return strategy.reduce(tf.distribute.ReduceOp.SUM,
......
research/object_detection/utils/colab_utils.py
View file @ 8f5f819f
......@@ -44,7 +44,7 @@ def image_from_numpy(image):
with io.BytesIO() as img_output:
Image.fromarray(image).save(img_output, format='JPEG')
data = output.getvalue()
data = img_output.getvalue()
data = str(base64.b64encode(data))[2:-1]
return data
......
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