Please read & provide the following

hi, i trian the model for 3000 ite, only to find the EVALU result to be zero. During the training time, there is no error, expcet a waring saying that
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
does it have something to do with the argu config.eval_only? i do not set the argument, so it should take the default
any available hep would be appreciated.

## Instructions To Reproduce the Issue:

1. Full runnable code or full changes you made:
i follow the turiroal , loading my datasets using register_coco_json() , then i start to train.
here is my .yaml file, the Base-RCNN-FPN.yaml is the original one in config directory

_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: ""
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 6
DATASETS:
TRAIN: ("steel_train",) #("coco_2017_train",)
TEST: ("steel_val",) #("coco_2017_val",)
DATALOADER:
NUM_WORKERS: 8
SOLVER:
STEPS: () #(210000, 250000)
MAX_ITER: 270000
IMS_PER_BATCH: 16
BASE_LR: 0.001 #0.02
MAX_ITER: 90000

2. in command line, i run
python plain_train_net.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml
and it run successfully as it seems

4. __Full logs__ or other relevant observations:
[04/19 04:37:58] d2.data.datasets.coco INFO: Loaded 360 images in COCO format from ../datasets/steel/annotations/steel_val.json
[04/19 04:37:58] d2.data.build INFO: Distribution of instances among all 6 categories:
| category | #instances | category | #instances | category | #instances |
|:-------------:|:-------------|:-------------:|:-------------|:----------:|:-------------|
| crazing | 154 | inclusion | 195 | patches | 170 |
| pitted_surf.. | 82 | rolled-in_s.. | 132 | scratches | 102 |
| | | | | | |
| total | 835 | | | | |
[04/19 04:37:58] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[04/19 04:37:58] d2.data.common INFO: Serializing the dataset using:
[04/19 04:37:58] d2.data.common INFO: Serializing 360 elements to byte tensors and concatenating them all ...
[04/19 04:37:58] d2.data.common INFO: Serialized dataset takes 0.19 MiB
[04/19 04:37:58] d2.evaluation.evaluator INFO: Start inference on 360 batches
[04/19 04:38:00] d2.evaluation.evaluator INFO: Inference done 11/360. Dataloading: 0.0006 s/iter. Inference: 0.0690 s/iter. Eval: 0.0027 s/iter. Total: 0.0723 s/iter. ETA=0:00:25
[04/19 04:38:05] d2.evaluation.evaluator INFO: Inference done 80/360. Dataloading: 0.0011 s/iter. Inference: 0.0684 s/iter. Eval: 0.0030 s/iter. Total: 0.0725 s/iter. ETA=0:00:20
[04/19 04:38:10] d2.evaluation.evaluator INFO: Inference done 151/360. Dataloading: 0.0012 s/iter. Inference: 0.0676 s/iter. Eval: 0.0028 s/iter. Total: 0.0717 s/iter. ETA=0:00:14
[04/19 04:38:15] d2.evaluation.evaluator INFO: Inference done 221/360. Dataloading: 0.0012 s/iter. Inference: 0.0679 s/iter. Eval: 0.0028 s/iter. Total: 0.0719 s/iter. ETA=0:00:09
[04/19 04:38:20] d2.evaluation.evaluator INFO: Inference done 290/360. Dataloading: 0.0012 s/iter. Inference: 0.0681 s/iter. Eval: 0.0028 s/iter. Total: 0.0721 s/iter. ETA=0:00:05
[04/19 04:38:25] d2.evaluation.evaluator INFO: Total inference time: 0:00:25.371791 (0.071470 s / iter per device, on 1 devices)
[04/19 04:38:25] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:23 (0.067332 s / iter per device, on 1 devices)
[04/19 04:38:25] d2.evaluation.coco_evaluation INFO: Preparing results for COCO format ...
[04/19 04:38:25] d2.evaluation.coco_evaluation INFO: Saving results to ./output/inference/steel_val/coco_instances_results.json
[04/19 04:38:25] d2.evaluation.coco_evaluation INFO: Evaluating predictions with unofficial COCO API...
[04/19 04:38:25] d2.evaluation.fast_eval_api INFO: Evaluate annotation type *bbox*
[04/19 04:38:25] d2.evaluation.fast_eval_api INFO: COCOeval_opt.evaluate() finished in 0.04 seconds.
[04/19 04:38:25] d2.evaluation.fast_eval_api INFO: Accumulating evaluation results...
[04/19 04:38:25] d2.evaluation.fast_eval_api INFO: COCOeval_opt.accumulate() finished in 0.02 seconds.
[04/19 04:38:25] d2.evaluation.coco_evaluation INFO: Evaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
[04/19 04:38:25] d2.evaluation.coco_evaluation INFO: Per-category bbox AP:
| category | AP | category | AP | category | AP |
|:---------------|:------|:----------------|:------|:-----------|:------|
| crazing | 0.000 | inclusion | 0.000 | patches | 0.000 |
| pitted_surface | 0.000 | rolled-in_scale | 0.000 | scratches | 0.000 |
[04/19 04:38:26] d2.evaluation.fast_eval_api INFO: Evaluate annotation type *segm*
[04/19 04:38:26] d2.evaluation.fast_eval_api INFO: COCOeval_opt.evaluate() finished in 0.13 seconds.
[04/19 04:38:26] d2.evaluation.fast_eval_api INFO: Accumulating evaluation results...
[04/19 04:38:26] d2.evaluation.fast_eval_api INFO: COCOeval_opt.accumulate() finished in 0.02 seconds.
[04/19 04:38:26] d2.evaluation.coco_evaluation INFO: Evaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
[04/19 04:38:26] d2.evaluation.coco_evaluation INFO: Per-category segm AP:
| category | AP | category | AP | category | AP |
|:---------------|:------|:----------------|:------|:-----------|:------|
| crazing | 0.000 | inclusion | 0.000 | patches | 0.000 |
| pitted_surface | 0.000 | rolled-in_scale | 0.000 | scratches | 0.000 |
[04/19 04:38:26] detectron2 INFO: Evaluation results for steel_val in csv format:
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: Task: bbox
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: Task: segm
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl
[04/19 04:38:26] d2.evaluation.testing INFO: copypaste: 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

## Expected behavior:

at least, there should be a result but zero, i don not know what cause the problem

## Environment:

the environment is set up following the tutorial
```
------------------------------- -----------------------------------------------------------------------------------
sys.platform linux
Python 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0]
numpy 1.23.5
detectron2 0.6 @/home/detectron2/detectron2
Compiler GCC 9.4
CUDA compiler CUDA 11.6
detectron2 arch flags 7.5
DETECTRON2_ENV_MODULE
PyTorch 1.12.1+cu116 @/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch
PyTorch debug build False
torch._C._GLIBCXX_USE_CXX11_ABI False
GPU available Yes
GPU 0 NVIDIA GeForce RTX 2080 Ti (arch=7.5)
Driver version 510.54
CUDA_HOME /usr/local/cuda
Pillow 9.3.0
torchvision 0.13.1+cu116 @/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore 0.1.5.post20221221
iopath 0.1.9
cv2 4.6.0
------------------------------- -----------------------------------------------------------------------------------

Start with plain_train_net.py and the custom YAML configuration, then trace dataset registration through register_coco_json() and the evaluation logs. Compare the registered category IDs, configured NUM_CLASSES, dataset annotations, and evaluation checkpoint; done means identifying the cause of the zero bbox and segm metrics and documenting a reproducible correction.

Written by the indexing model from the issue text.