No 'instances_train2014.json' File Found Error Issue

[msson]

Hi,

I am trying to train for coco2014 dataset. (I just followed this repo's training example)

I put coco dataset on home/mson/download/Mask_RCNN/coco/train2014/(image files..) home/mson/download/Mask_RCNN/coco/val2014/(image files..) home/mson/download/Mask_RCNN/coco/annotations/(.json files including 'instances_train2014.json')

The command I am using is: python coco.py train --dataset=/path/to/coco/ --model=coco

The result I can see is that FileNotFoundError: [Errno 2] No such file or directory: '/path/to/coco/annotations /instances_train2014.json'

Can anyone please help? Please see the logs below.

/home/mson/anaconda3/envs/tf/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type. from ._conv import register_converters as _register_converters Using TensorFlow backend. Command: train Model: coco Dataset: /path/to/coco/ Year: 2014 Logs: /home/mson/download/Mask_RCNN/logs Auto Download: False

Configurations: BACKBONE_SHAPES [[256 256] [128 128] [ 64 64] [ 32 32] [ 16 16]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 2 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.7 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 2 IMAGE_MAX_DIM 1024 IMAGE_MIN_DIM 800 IMAGE_PADDING True IMAGE_SHAPE [1024 1024 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME coco NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (32, 64, 128, 256, 512) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001

Loading weights /home/mson/download/Mask_RCNN/mask_rcnn_coco.h5 2018-02-09 16:07:30.877009: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 2018-02-09 16:07:31.080556: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:895] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-02-09 16:07:31.080894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties: name: Quadro K5200 major: 3 minor: 5 memoryClockRate(GHz): 0.771 pciBusID: 0000:02:00.0 totalMemory: 7.93GiB freeMemory: 7.51GiB 2018-02-09 16:07:31.080922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Quadro K5200, pci bus id: 0000:02:00.0, compute capability: 3.5) loading annotations into memory... Traceback (most recent call last): File "coco.py", line 476, in dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download) File "coco.py", line 108, in loadcoco coco = COCO("{}annotations/instances{}{}.json".format(dataset_dir, subset, year)) File "/home/mson/anaconda3/envs/tf/lib/python3.6/site-packages/pycocotools/coco.py", line 79, in init dataset = json.load(open(annotation_file, 'r')) FileNotFoundError: [Errno 2] No such file or directory: '/path/to/coco/annotations/instances_train2014.json'

[msson]

I tried to solve this prob for two days please help me thanks!

[monomon]

You need to pass the path to coco as the --dataset argument, /path/to/coco/ has to become /home/mson/download/Mask_RCNN/coco, which gives you

python coco.py train --dataset=/home/mson/download/Mask_RCNN/coco --model=coco

You do not think coco will magically find your dataset, right?

[Alvin1994]

@msson , As @monomon said, that is the right way to pass coco data set, if it still failed. You need to remove the json file manually and let the script download again.

[sumodm]

Another potential issue if you give --dataset=~/<path>, it won't work as it is unable to expand '~' to /home/user/.

[SubhayanDas08]

https://github.com/fizyr/keras-retinanet-test-data/blob/main/coco/annotations/instances_train2017.json

You will find the json file from here Download and add it in the given location