trying to clone a dummy repo

README.md

@@ -1,12 +1,14 @@
 ---
-title: Odor Mmdetection
-emoji: 🦀
-colorFrom: red
+title: MMDetection
+emoji: 🔥
+colorFrom: pink
 colorTo: purple
 sdk: gradio
-sdk_version: 3.29.0
+python_version: 3.9.13
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
+duplicated_from: hysts/mmdetection
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -31,7 +31,9 @@ This is an unofficial demo for [https://github.com/open-mmlab/mmdetection](https
 
 DEFAULT_MODEL_TYPE = 'detection'
 DEFAULT_MODEL_NAMES = {
-    'detection': 'Faster R-CNN (R-50-FPN)',
+    'detection': 'YOLOX-l',
+    'instance_segmentation': 'QueryInst (R-50-FPN)',
+    'panoptic_segmentation': 'MaskFormer (R-50)',
 }
 DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE]
 
@@ -41,13 +43,6 @@ def extract_tar() -> None:
         return
     with tarfile.open('mmdet_configs/configs.tar') as f:
         f.extractall('mmdet_configs')
-        
-def update_visualization_score_threshold(model_type: str) -> dict:
-    return gr.Slider.update(visible=model_type != 'panoptic_segmentation')
-
-
-def update_redraw_button(model_type: str) -> dict:
-    return gr.Button.update(visible=model_type != 'panoptic_segmentation')
 
 
 def update_input_image(image: np.ndarray) -> dict:
@@ -66,6 +61,14 @@ def update_model_name(model_type: str) -> dict:
     return gr.Dropdown.update(choices=model_names, value=model_name)
 
 
+def update_visualization_score_threshold(model_type: str) -> dict:
+    return gr.Slider.update(visible=model_type != 'panoptic_segmentation')
+
+
+def update_redraw_button(model_type: str) -> dict:
+    return gr.Button.update(visible=model_type != 'panoptic_segmentation')
+
+
 def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
 
@@ -127,7 +130,6 @@ with gr.Blocks(css='style.css') as demo:
                       outputs=redraw_button)
 
     model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
-    print(model_name, visualization_score_threshold, "HERE")
     run_button.click(fn=model.run,
                      inputs=[
                          model_name,
@@ -149,4 +151,4 @@ with gr.Blocks(css='style.css') as demo:
                          inputs=example_images,
                          outputs=input_image)
 
-demo.queue().launch(show_api=False)
+demo.queue().launch(show_api=False)

images/README.md

@@ -0,0 +1,9 @@
+These images are freely-usable ones from https://www.pexels.com/.
+
+- https://www.pexels.com/photo/assorted-color-kittens-45170/
+- https://www.pexels.com/photo/white-wooden-kitchen-cabinet-1599791/
+- https://www.pexels.com/photo/assorted-books-on-book-shelves-1370295/
+- https://www.pexels.com/photo/pile-of-assorted-varieties-of-vegetables-2255935/
+- https://www.pexels.com/photo/sliced-fruits-on-tray-1132047/
+- https://www.pexels.com/photo/group-of-people-carrying-surfboards-1549196/
+- https://www.pexels.com/photo/aerial-photo-of-vehicles-in-the-city-1031698/

mmdet_configs/LICENSE

@@ -0,0 +1,203 @@
+Copyright 2018-2023 OpenMMLab. All rights reserved.
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mmdet_configs/README.md

@@ -0,0 +1,2 @@
+`configs.tar` is a tarball of https://github.com/open-mmlab/mmdetection/tree/v2.25.0/configs.
+The license file of the mmdetection is also included in this directory.

model.py

@@ -42,7 +42,11 @@ def _update_model_dict_if_hf_token_is_given(
 
 class Model:
     DETECTION_MODEL_DICT = _load_model_dict('model_dict/detection.yaml')
-    MODEL_DICT = DETECTION_MODEL_DICT
+    INSTANCE_SEGMENTATION_MODEL_DICT = _load_model_dict(
+        'model_dict/instance_segmentation.yaml')
+    PANOPTIC_SEGMENTATION_MODEL_DICT = _load_model_dict(
+        'model_dict/panoptic_segmentation.yaml')
+    MODEL_DICT = DETECTION_MODEL_DICT | INSTANCE_SEGMENTATION_MODEL_DICT | PANOPTIC_SEGMENTATION_MODEL_DICT
 
     def __init__(self, model_name: str):
         self.device = torch.device(
@@ -106,4 +110,4 @@ class AppModel(Model):
                                         list[list[np.ndarray]]]
                | dict[str, np.ndarray], np.ndarray]:
         self.set_model(model_name)
-        return self.detect_and_visualize(image, score_threshold)
+        return self.detect_and_visualize(image, score_threshold)

model_dict/detection.yaml

@@ -3,4 +3,40 @@ Faster R-CNN (R-50-FPN):
     model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth
 Faster R-CNN (X-101-64x4d-FPN):
     config: https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
+    model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
+SSD (VGG16):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth
+RetinaNet (X-101-64x4d-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth
+YOLOv3 (DarkNet-53 608):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth
+CornerNet (HourglassNet-104):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth
+FCOS (X-101):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth
+DETR:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth
+YOLOX-tiny:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_tiny_8x8_300e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth
+YOLOX-s:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
+YOLOX-l:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_l_8x8_300e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
+YOLOX-x:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_x_8x8_300e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth
+Deformable DETR (R-50 two-stage Deformable DETR):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth
+TOOD (R-101-dcnv2):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728-4a824142.pth

model_dict/instance_segmentation.yaml

@@ -0,0 +1,51 @@
+Mask R-CNN (R-50-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
+Mask R-CNN (X-101-64x4d-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth
+Cascade Mask R-CNN (X-101-64x4d-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth
+Mask Scoring R-CNN (R-X101-64x4d):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth
+HTC (X-101-64x4d-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth
+YOLACT:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolact/yolact_r50_1x8_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth
+Instaboost (Mask R-CNN (X-101-64x4d-FPN)):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth
+SOLO:
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solo/solo_r50_fpn_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_3x_coco/solo_r50_fpn_3x_coco_20210901_012353-11d224d7.pth
+PointRend (R-50-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth
+DetectoRS (HTC + ResNet-101):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth
+SOLOv2 (R-50):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r50_fpn_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856-fed092d4.pth
+SOLOv2 (X-101 (DCN)):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_x101_dcn_fpn_3x_coco/solov2_x101_dcn_fpn_3x_coco_20220513_214337-aef41095.pth
+SCNet (X-101-64x4d-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
+QueryInst (R-50-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth
+QueryInst (R-101-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth
+Mask2Former (R-50):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth
+Mask2Former (Swin-S):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth

model_dict/panoptic_segmentation.yaml

@@ -0,0 +1,15 @@
+Panoptic FPN (R-50-FPN):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth
+MaskFormer (R-50):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth
+MaskFormer (Swin-L):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth
+Mask2Former (R-50):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth
+Mask2Former (Swin-L):
+    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth

requirements.txt

@@ -1,8 +1,7 @@
-typing
 mmcv-full==1.5.2
 mmdet==2.25.0
 numpy==1.22.4
 opencv-python-headless==4.5.5.64
 openmim==0.1.5
 torch==1.11.0
-torchvision==0.12.0
+torchvision==0.12.0

style.css

@@ -10,4 +10,4 @@ img#overview {
 img#visitor-badge {
   display: block;
   margin: auto;
-}
+}