Open songzenghui opened 6 years ago
Inference is show anything on image? I also have some issue, mask and rcnn shows up zero or related mAP, but inference seems good.
where is the TEST in the yaml file?
Here is my dataset path in dataset_catalog.py
:
'coco_2014_train': {
_IM_DIR:
_DATA_DIR + '/car-1w/train_1w',
_ANN_FN:
_DATA_DIR + '/car-1w/train_1w.json'
},
'coco_2014_val': {
_IM_DIR:
_DATA_DIR + '/car/images',
_ANN_FN:
_DATA_DIR + '/car/annotations/train.json'
},
And here is my total yaml
file including TEST:
TEST:
DATASETS: ('coco_2014_val',)
SCALE: 500
MAX_SIZE: 833
NMS: 0.5
RPN_PRE_NMS_TOP_N: 1000 # Per FPN level
RPN_POST_NMS_TOP_N: 1000
And here is my result of test_net.py
:
INFO json_dataset_evaluator.py: 222: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~
INFO json_dataset_evaluator.py: 223: 0.0
INFO json_dataset_evaluator.py: 231: 0.0
INFO json_dataset_evaluator.py: 232: ~~~~ Summary metrics ~~~~
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO json_dataset_evaluator.py: 199: Wrote json eval results to: ./test/coco_2014_val/generalized_rcnn/detection_results.pkl
INFO task_evaluation.py: 62: Evaluating bounding boxes is done!
INFO task_evaluation.py: 181: copypaste: Dataset: coco_2014_val
INFO task_evaluation.py: 183: copypaste: Task: box
INFO task_evaluation.py: 186: copypaste: AP,AP50,AP75,APs,APm,APl
INFO task_evaluation.py: 187: copypaste: 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
Should i modify some parameters? Like max-iters, max-size,lr??? and so on?
STEPS: [0, 30000, 40000]
I guess whether the STEPS is too small so the LR updates too early.
Thanks to your reply.Could u tell me the meaning of STEPS? WHY it is a list and WHAT is the meaning of the number in the list? @lilichu
the LR will update when the iters reach the STEPS.
BASE_LR: 0.0025
GAMMA: 0.1
MAX_ITER: 200000
STEPS: [0, 30000, 40000]
For example, it will change to 0.00025 after 30000 iters and 0.000025 after 40000 iters in your example above. @songzenghui
Thank you @lilichu I will try
@songzenghui Hi: I guess your data format is wrong.
In your config file, dumb question:
Is WEIGHTS is under TRAIN? Like this:
TRAIN: ---- WEIGHTS: /tmp/detectron-download-cache/ImageNetPretrained/MSRA/R-50.pkl
Or is it under TEST?
hey guys i met same problems on my own datasets, almost got zero map Did u solve it? wish your reply
@songzenghui Hello! I also run test_net.py on COCO dataset. However, when the process finished, I didn't get the meric(AP or AR), just got a .pkl file. Could you tell me how do you get AP?
I use faster-rcnn-50-rpn to train my own dataset which only has one class ,but the result is very poor.I use the
test_net.py
to get the mAP score,and it got zero!!! The figure's size is all 1069*500.Train dataset 's size is 10,000+. I run the model for 200,000 iters. Here is myyaml
file:MODEL:
TYPE: generalized_rcnn
CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
NUM_CLASSES: 2
FASTER_RCNN: True
NUM_GPUS: 1
SOLVER:
WEIGHT_DECAY: 0.0001
LR_POLICY: steps_with_decay
BASE_LR: 0.0025
GAMMA: 0.1
MAX_ITER: 200000
STEPS: [0, 30000, 40000]
FPN:
FPN_ON: True
MULTILEVEL_ROIS: True
MULTILEVEL_RPN: True
FAST_RCNN:
ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 7
ROI_XFORM_SAMPLING_RATIO: 2
TRAIN:
WEIGHTS: /tmp/detectron-download-cache/ImageNetPretrained/MSRA/R-50.pkl
DATASETS: ('coco_2014_train',)
SCALES: (500,)
MAX_SIZE: 833
BATCH_SIZE_PER_IM: 256
RPN_PRE_NMS_TOP_N: 2000 # Per FPN level
OUTPUT_DIR: .
And i modify the
dataset_catalog.py
to point my dataset