Open kamilc opened 2 years ago
Hello,did you sove the problem?
I trained the model step by step:
but I get the result: { "bbox": { "AP": 0.6086382577102198, "AP50": 0.9686674621591191, "AP75": 0.651878918279263, "APs": 0.16141502940083804, "APm": 0.397410775472805, "APl": 0.8720534612187981, "AP-person": 0.4201384547180321, "AP-bicycle": 0.036003600360036005, "AP-car": 6.707690074690357, "AP-motorcycle": 0.23102310231023102, "AP-airplane": 12.85165286472409, "AP-bus": 18.615992723296102, "AP-train": 9.025457589149143, "AP-truck": 0.575665427910193, "AP-boat": 0.001288515813115742, "AP-traffic light": 0.0, "AP-fire hydrant": 0.0, "AP-stop sign": 0.0, "AP-parking meter": 0.0, "AP-bench": 0.00584188632494989, "AP-bird": 0.0, "AP-cat": 0.0, "AP-dog": 0.220306377521335, "AP-horse": 0.0, "AP-sheep": 0.0, "AP-cow": 0.0, "AP-elephant": 0.0, "AP-bear": 0.0, "AP-zebra": 0.0, "AP-giraffe": 0.0, "AP-backpack": 0.0, "AP-umbrella": 0.0, "AP-handbag": 0.0, "AP-tie": 0.0, "AP-suitcase": 0.0, "AP-frisbee": 0.0, "AP-skis": 0.0, "AP-snowboard": 0.0, "AP-sports ball": 0.0, "AP-kite": 0.0, "AP-baseball bat": 0.0, "AP-baseball glove": 0.0, "AP-skateboard": 0.0, "AP-surfboard": 0.0, "AP-tennis racket": 0.0, "AP-bottle": 0.0, "AP-wine glass": 0.0, "AP-cup": 0.0, "AP-fork": 0.0, "AP-knife": 0.0, "AP-spoon": 0.0, "AP-bowl": 0.0, "AP-banana": 0.0, "AP-apple": 0.0, "AP-sandwich": 0.0, "AP-orange": 0.0, "AP-broccoli": 0.0, "AP-carrot": 0.0, "AP-hot dog": 0.0, "AP-pizza": 0.0, "AP-donut": 0.0, "AP-cake": 0.0, "AP-chair": 0.0, "AP-couch": 0.0, "AP-potted plant": 0.0, "AP-bed": 0.0, "AP-dining table": 0.0, "AP-toilet": 0.0, "AP-tv": 0.0, "AP-laptop": 0.0, "AP-mouse": 0.0, "AP-remote": 0.0, "AP-keyboard": 0.0, "AP-cell phone": 0.0, "AP-microwave": 0.0, "AP-oven": 0.0, "AP-toaster": 0.0, "AP-sink": 0.0, "AP-refrigerator": 0.0, "AP-book": 0.0, "AP-clock": 0.0, "AP-vase": 0.0, "AP-scissors": 0.0, "AP-teddy bear": 0.0, "AP-hair drier": 0.0, "AP-toothbrush": 0.0 }, "segm": { "AP": 0.591411294434657, "AP50": 0.9301556758380742, "AP75": 0.6180622877437737, "APs": 0.07606327093023611, "APm": 0.359037156413112, "APl": 0.9040012941146236, "AP-person": 0.0, "AP-bicycle": 0.0, "AP-car": 6.47596948525144, "AP-motorcycle": 0.18151815181518152, "AP-airplane": 9.893301675508773, "AP-bus": 19.9174939425781, "AP-train": 10.238609475394998, "AP-truck": 0.5967282715647985, "AP-boat": 0.0015103648841640705, "AP-traffic light": 0.0, "AP-fire hydrant": 0.0, "AP-stop sign": 0.0, "AP-parking meter": 0.0, "AP-bench": 0.004966239164042062, "AP-bird": 0.0, "AP-cat": 0.0, "AP-dog": 0.0028059486110554375, "AP-horse": 0.0, "AP-sheep": 0.0, "AP-cow": 0.0, "AP-elephant": 0.0, "AP-bear": 0.0, "AP-zebra": 0.0, "AP-giraffe": 0.0, "AP-backpack": 0.0, "AP-umbrella": 0.0, "AP-handbag": 0.0, "AP-tie": 0.0, "AP-suitcase": 0.0, "AP-frisbee": 0.0, "AP-skis": 0.0, "AP-snowboard": 0.0, "AP-sports ball": 0.0, "AP-kite": 0.0, "AP-baseball bat": 0.0, "AP-baseball glove": 0.0, "AP-skateboard": 0.0, "AP-surfboard": 0.0, "AP-tennis racket": 0.0, "AP-bottle": 0.0, "AP-wine glass": 0.0, "AP-cup": 0.0, "AP-fork": 0.0, "AP-knife": 0.0, "AP-spoon": 0.0, "AP-bowl": 0.0, "AP-banana": 0.0, "AP-apple": 0.0, "AP-sandwich": 0.0, "AP-orange": 0.0, "AP-broccoli": 0.0, "AP-carrot": 0.0, "AP-hot dog": 0.0, "AP-pizza": 0.0, "AP-donut": 0.0, "AP-cake": 0.0, "AP-chair": 0.0, "AP-couch": 0.0, "AP-potted plant": 0.0, "AP-bed": 0.0, "AP-dining table": 0.0, "AP-toilet": 0.0, "AP-tv": 0.0, "AP-laptop": 0.0, "AP-mouse": 0.0, "AP-remote": 0.0, "AP-keyboard": 0.0, "AP-cell phone": 0.0, "AP-microwave": 0.0, "AP-oven": 0.0, "AP-toaster": 0.0, "AP-sink": 0.0, "AP-refrigerator": 0.0, "AP-book": 0.0, "AP-clock": 0.0, "AP-vase": 0.0, "AP-scissors": 0.0, "AP-teddy bear": 0.0, "AP-hair drier": 0.0, "AP-toothbrush": 0.0 } }
I don't know how to train the last step?
How to do the last step?
python tools/run_train.py --config-file path_to_config_file --method pure-metric-averaged
How to do the last step?
python tools/run_train.py --config-file path_to_config_file --method pure-metric-averaged
Hi, do you solve this problem? I have the same problem with you.
Hi,
Thank you so much for an amazing paper and for publishing this code!
I have a question regarding the way to use one of your trained models in a N-way K-shot manner. I can see the repository closely follows
detectron2
. This means that I can load the model's weights and get predictions viaDefaultPredictor
. That class's__call__
only takes an input image though. I'm struggling to see the way to incorporate the N*K embeddings (and ways to get them in the first place) of the support set, and then to use the predictor to get the results. (I've downloaded the weights for themask_rcnn_R_50_FPN_ft_fullclsag_cos_bh_all_5shot_manyiters_V2_correct
config).I'm very likely not seeing something very obvious, apologies for asking naive questions in this case. I'll appreciate any pointers on ways to use it in the N-way K-shot manner though!
Thank you so much again