Training on custom dataset, but detectron keeps looking for 'datasets/coco/annotations/instances_train2017.json'

I'm training with a custom dataset, but for some reason when training (both locally and on google colab) using

trainer = DefaultTrainer(cfg) 
trainer.resume_or_load(resume=False)
trainer.train()

I receive the error

FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_train2017.json'

(full error below).

I'd appreciate any help as to why this might happen. Thank you!

Instructions To Reproduce the Issue:

Here is the code for setting up my cfg:

from detectron2.engine import DefaultTrainer

cfg = get_cfg()

DatasetCatalog.register("train", lambda: detectron.create_detectron_img_dict_list(fpath_json_train))
DatasetCatalog.register("val", lambda: detectron.create_detectron_img_dict_list(fpath_json_valid))

MetadataCatalog.get("train").set(thing_classes=classes.values())
MetadataCatalog.get("val").set(thing_classes=classes.values())

# microcontroller_metadata = MetadataCatalog.get("trash_train")
metadata_train = MetadataCatalog.get("train")
metadata_valid = MetadataCatalog.get("val")

cfg.DATASETS.TRAIN = ("train",)
cfg.DATASETS.TEST = ()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_C4_1x.yaml"))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_C4_1x.yaml")  # Let training initialize from model zoo
cfg.DATALOADER.NUM_WORKERS = 2
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025  # pick a good LR
cfg.SOLVER.MAX_ITER = 1800    # 300 iterations seems good enough for the tutorial dataset; you will need to train longer for a practical dataset
cfg.SOLVER.STEPS = []        # do not decay learning rate
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # faster, and good enough for this toy dataset (default: 512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1  # only has one class (ballon). (see https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets)
# # NOTE: this config means the number of classes, but a few popular unofficial tutorials incorrect uses num_classes+1 here.
#
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)

Here is an example of the json for a subset of my dataset:

{
    "info": {
        "year": 2022,
        "version": null,
        "description": "dataset created from yolo /content/drive/MyDrive/datasets/dataset_05_mini_gnejna",
        "contributor": "BIOBLU project, University of Malta",
        "date_created": "2022-04-29"
    },
    "images": [
        {
            "id": 0,
            "width": 1824,
            "height": 1825,
            "file_name": "/content/drive/MyDrive/datasets/dataset_05_mini_gnejna_detectron/test/Gnejna_DJI_0684_0-2.tif",
            "license": null,
            "flickr_url": null,
            "coco_url": null,
            "date_captured": null
        },
        {
            "id": 1,
            "width": 1825,
            "height": 1825,
            "file_name": "/content/drive/MyDrive/datasets/dataset_05_mini_gnejna_detectron/test/Gnejna_DJI_0687_0-0.tif",
            "license": null,
            "flickr_url": null,
            "coco_url": null,
            "date_captured": null
        },
        {
            "id": 2,
            "width": 1825,
            "height": 1825,
            "file_name": "/content/drive/MyDrive/datasets/dataset_05_mini_gnejna_detectron/test/Gnejna_DJI_0688_0-0.tif",
            "license": null,
            "flickr_url": null,
            "coco_url": null,
            "date_captured": null
        },
        {
            "id": 3,
            "width": 1824,
            "height": 1825,
            "file_name": "/content/drive/MyDrive/datasets/dataset_05_mini_gnejna_detectron/test/Gnejna_DJI_0691_0-2.tif",
            "license": null,
            "flickr_url": null,
            "coco_url": null,
            "date_captured": null
        },
        {
            "id": 4,
            "width": 1825,
            "height": 1825,
            "file_name": "/content/drive/MyDrive/datasets/dataset_05_mini_gnejna_detectron/test/Gnejna_DJI_0692_0-1.tif",
            "license": null,
            "flickr_url": null,
            "coco_url": null,
            "date_captured": null
        }
    ],
    "annotations": [
        {
            "id": 0,
            "image_id": 0,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                157,
                960,
                71,
                109
            ],
            "bbox_mode": 1
        },
        {
            "id": 1,
            "image_id": 0,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                428,
                789,
                69,
                73
            ],
            "bbox_mode": 1
        },
        {
            "id": 2,
            "image_id": 3,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                177,
                227,
                33,
                17
            ],
            "bbox_mode": 1
        },
        {
            "id": 3,
            "image_id": 3,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                659,
                520,
                51,
                64
            ],
            "bbox_mode": 1
        },
        {
            "id": 4,
            "image_id": 4,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                992,
                520,
                29,
                19
            ],
            "bbox_mode": 1
        },
        {
            "id": 5,
            "image_id": 4,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                613,
                1719,
                11,
                9
            ],
            "bbox_mode": 1
        },
        {
            "id": 6,
            "image_id": 4,
            "category_id": 0,
            "segmentation": [],
            "area": null,
            "iscrowd": 0,
            "bbox": [
                1482,
                825,
                55,
                80
            ],
            "bbox_mode": 1
        }
    ],
    "categories": [
        {
            "id": 0,
            "name": "trash",
            "supercategory": "trash"
        }
    ],
    "licences": []
}

And here are the contents of the cfg:

CUDNN_BENCHMARK: false
DATALOADER:
  ASPECT_RATIO_GROUPING: true
  FILTER_EMPTY_ANNOTATIONS: true
  NUM_WORKERS: 2
  REPEAT_THRESHOLD: 0.0
  SAMPLER_TRAIN: TrainingSampler
DATASETS:
  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
  PROPOSAL_FILES_TEST: []
  PROPOSAL_FILES_TRAIN: []
  TEST:
  - coco_2017_val
  TRAIN:
  - coco_2017_train
GLOBAL:
  HACK: 1.0
INPUT:
  CROP:
    ENABLED: false
    SIZE:
    - 0.9
    - 0.9
    TYPE: relative_range
  FORMAT: BGR
  MASK_FORMAT: polygon
  MAX_SIZE_TEST: 1333
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MIN_SIZE_TRAIN:
  - 640
  - 672
  - 704
  - 736
  - 768
  - 800
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
MODEL:
  ANCHOR_GENERATOR:
    ANGLES:
    - - -90
      - 0
      - 90
    ASPECT_RATIOS:
    - - 0.5
      - 1.0
      - 2.0
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES:
    - - 32
      - 64
      - 128
      - 256
      - 512
  BACKBONE:
    FREEZE_AT: 2
    NAME: build_resnet_backbone
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES: []
    NORM: ''
    OUT_CHANNELS: 256
  KEYPOINT_ON: false
  LOAD_PROPOSALS: false
  MASK_ON: false
  META_ARCHITECTURE: GeneralizedRCNN
  PANOPTIC_FPN:
    COMBINE:
      ENABLED: true
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
    INSTANCE_LOSS_WEIGHT: 1.0
  PIXEL_MEAN:
  - 103.53
  - 116.28
  - 123.675
  PIXEL_STD:
  - 1.0
  - 1.0
  - 1.0
  PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: RPN
  RESNETS:
    DEFORM_MODULATED: false
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE:
    - false
    - false
    - false
    - false
    DEPTH: 50
    NORM: FrozenBN
    NUM_GROUPS: 1
    OUT_FEATURES:
    - res4
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: true
    WIDTH_PER_GROUP: 64
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: &id001
    - 1.0
    - 1.0
    - 1.0
    - 1.0
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES:
    - p3
    - p4
    - p5
    - p6
    - p7
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.4
    - 0.5
    NMS_THRESH_TEST: 0.5
    NORM: ''
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS:
    - - 10.0
      - 10.0
      - 5.0
      - 5.0
    - - 20.0
      - 20.0
      - 10.0
      - 10.0
    - - 30.0
      - 30.0
      - 15.0
      - 15.0
    IOUS:
    - 0.5
    - 0.6
    - 0.7
  ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS:
    - 10.0
    - 10.0
    - 5.0
    - 5.0
    CLS_AGNOSTIC_BBOX_REG: false
    CONV_DIM: 256
    FC_DIM: 1024
    FED_LOSS_FREQ_WEIGHT_POWER: 0.5
    FED_LOSS_NUM_CLASSES: 50
    NAME: ''
    NORM: ''
    NUM_CONV: 0
    NUM_FC: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: false
    USE_FED_LOSS: false
    USE_SIGMOID_CE: false
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 128
    IN_FEATURES:
    - res4
    IOU_LABELS:
    - 0
    - 1
    IOU_THRESHOLDS:
    - 0.5
    NAME: Res5ROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 1
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: true
    SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
    CONV_DIMS:
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: false
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: ''
    NUM_CONV: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id001
    BOUNDARY_THRESH: -1
    CONV_DIMS:
    - -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES:
    - res4
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.3
    - 0.7
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 2000
    PRE_NMS_TOPK_TEST: 6000
    PRE_NMS_TOPK_TRAIN: 12000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
  AMP:
    ENABLED: false
  BASE_LR: 0.00025
  BASE_LR_END: 0.0
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 5000
  CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: false
    NORM_TYPE: 2.0
  GAMMA: 0.1
  IMS_PER_BATCH: 2
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 1800
  MOMENTUM: 0.9
  NESTEROV: false
  REFERENCE_WORLD_SIZE: 0
  STEPS: []
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: null
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: false
    FLIP: true
    MAX_SIZE: 4000
    MIN_SIZES:
    - 400
    - 500
    - 600
    - 700
    - 800
    - 900
    - 1000
    - 1100
    - 1200
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 0
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: false
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0

Full error:

---------------------------------------------------------------------------

FileNotFoundError                         Traceback (most recent call last)

[<ipython-input-10-6369bea6f979>](https://localhost:8080/#) in <module>()
----> 1 trainer = DefaultTrainer(cfg)
      2 trainer.resume_or_load(resume=False)
      3 trainer.train()

10 frames

[/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py](https://localhost:8080/#) in __init__(self, cfg)
    376         model = self.build_model(cfg)
    377         optimizer = self.build_optimizer(cfg, model)
--> 378         data_loader = self.build_train_loader(cfg)
    379 
    380         model = create_ddp_model(model, broadcast_buffers=False)

[/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py](https://localhost:8080/#) in build_train_loader(cls, cfg)
    545         Overwrite it if you'd like a different data loader.
    546         """
--> 547         return build_detection_train_loader(cfg)
    548 
    549     @classmethod

[/usr/local/lib/python3.7/dist-packages/detectron2/config/config.py](https://localhost:8080/#) in wrapped(*args, **kwargs)
    205             def wrapped(*args, **kwargs):
    206                 if _called_with_cfg(*args, **kwargs):
--> 207                     explicit_args = _get_args_from_config(from_config, *args, **kwargs)
    208                     return orig_func(**explicit_args)
    209                 else:

[/usr/local/lib/python3.7/dist-packages/detectron2/config/config.py](https://localhost:8080/#) in _get_args_from_config(from_config_func, *args, **kwargs)
    243             if name not in supported_arg_names:
    244                 extra_kwargs[name] = kwargs.pop(name)
--> 245         ret = from_config_func(*args, **kwargs)
    246         # forward the other arguments to __init__
    247         ret.update(extra_kwargs)

[/usr/local/lib/python3.7/dist-packages/detectron2/data/build.py](https://localhost:8080/#) in _train_loader_from_config(cfg, mapper, dataset, sampler)
    342             if cfg.MODEL.KEYPOINT_ON
    343             else 0,
--> 344             proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
    345         )
    346         _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0])

[/usr/local/lib/python3.7/dist-packages/detectron2/data/build.py](https://localhost:8080/#) in get_detection_dataset_dicts(names, filter_empty, min_keypoints, proposal_files, check_consistency)
    239         names = [names]
    240     assert len(names), names
--> 241     dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
    242     for dataset_name, dicts in zip(names, dataset_dicts):
    243         assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)

[/usr/local/lib/python3.7/dist-packages/detectron2/data/build.py](https://localhost:8080/#) in <listcomp>(.0)
    239         names = [names]
    240     assert len(names), names
--> 241     dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
    242     for dataset_name, dicts in zip(names, dataset_dicts):
    243         assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)

[/usr/local/lib/python3.7/dist-packages/detectron2/data/catalog.py](https://localhost:8080/#) in get(self, name)
     56                 )
     57             ) from e
---> 58         return f()
     59 
     60     def list(self) -> List[str]:

[/usr/local/lib/python3.7/dist-packages/detectron2/data/datasets/coco.py](https://localhost:8080/#) in <lambda>()
    498     assert isinstance(image_root, (str, os.PathLike)), image_root
    499     # 1. register a function which returns dicts
--> 500     DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
    501 
    502     # 2. Optionally, add metadata about this dataset,

[/usr/local/lib/python3.7/dist-packages/detectron2/data/datasets/coco.py](https://localhost:8080/#) in load_coco_json(json_file, image_root, dataset_name, extra_annotation_keys)
     67     json_file = PathManager.get_local_path(json_file)
     68     with contextlib.redirect_stdout(io.StringIO()):
---> 69         coco_api = COCO(json_file)
     70     if timer.seconds() > 1:
     71         logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))

[/usr/local/lib/python3.7/dist-packages/pycocotools/coco.py](https://localhost:8080/#) in __init__(self, annotation_file)
     79             print('loading annotations into memory...')
     80             tic = time.time()
---> 81             with open(annotation_file, 'r') as f:
     82                 dataset = json.load(f)
     83             assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))

FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_train2017.json'

Expected behavior:

Start training on my custom dataset instead of looking for coco2017.

Environment:

2022-04-29 12:41:31 URL:https://raw.githubusercontent.com/facebookresearch/detectron2/main/detectron2/utils/collect_env.py [8391/8391] -> "collect_env.py" [1]
----------------------  ----------------------------------------------------------------
sys.platform            linux
Python                  3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]
numpy                   1.21.6
detectron2              0.6 @/usr/local/lib/python3.7/dist-packages/detectron2
Compiler                GCC 7.5
CUDA compiler           CUDA 11.1
detectron2 arch flags   3.7
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.11.0+cu113 @/usr/local/lib/python3.7/dist-packages/torch
PyTorch debug build     False
GPU available           Yes
GPU 0                   Tesla K80 (arch=3.7)
Driver version          460.32.03
CUDA_HOME               /usr/local/cuda
Pillow                  7.1.2
torchvision             0.12.0+cu113 @/usr/local/lib/python3.7/dist-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore                  0.1.5.post20220414
iopath                  0.1.9
cv2                     4.1.2
----------------------  ----------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

facebookresearch / detectron2

Training on custom dataset, but detectron keeps looking for 'datasets/coco/annotations/instances_train2017.json' #4194

Instructions To Reproduce the Issue:

Expected behavior:

Environment: