andreivicol commented 2 months ago

I'm trying to train detectron2 for object detection on a custom dataset consisting of 2000 480 x 352 plant images. The dataset is in COCO format. I've run object detection training on YOLOv8 using the same dataset with a time of 7 minutes per epoch on CPU and about 2 minutes per epoch using MPS.

However, on detectron2 I'm seeing incredibly long training times on CPU and cannot get it to work on GPU. Any advice would be welcome. I'd really like to not have to switch to a Windows machine for this, if possible.

I suspect this might be a configuration issue.

Full runnable code or full changes you made:

if __name__ == "__main__":

setup_logger()
register_coco_instances("train", {},
                        "train.json",
                        "pTrain_images")
register_coco_instances("valid", {},
                        "val.json",
                        "pVal_images")

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml"))
cfg.DATASETS.TRAIN = ("train",)
cfg.DATASETS.TEST = ("valid",)
cfg.OUTPUT_DIR = "det2Out"

cfg.DATALOADER.NUM_WORKERS = 8
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
    "COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
cfg.SOLVER.IMS_PER_BATCH = 4
cfg.SOLVER.BASE_LR = 0.0001

cfg.SOLVER.WARMUP_ITERS = 500
# cfg.SOLVER.MAX_ITER = 1500
# cfg.SOLVER.STEPS = (1000, 1500)
cfg.SOLVER.STEPS = (30000,)
cfg.SOLVER.GAMMA = 0.1

cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 64
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.DEVICE = "cpu"
cfg.TEST.EVAL_PERIOD = 500

class CocoTrainer(DefaultTrainer):

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        if output_folder is None:
            os.makedirs("coco_eval", exist_ok=True)
            output_folder = "coco_eval"

        return COCOEvaluator(dataset_name, cfg, False, output_folder)

os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = CocoTrainer(cfg)
trainer.resume_or_load(resume=False)
trainer.train()

What exact command you run:

I'm training in PyCharm.

Full logs or other relevant observations:


[04/17 13:20:38 d2.data.datasets.coco]: Loaded 2952 images in COCO format from /Users/andreivicol/Doctorat/An1/YOLO/scripts/dataset/input/pTrain_masks/train.json
GeneralizedRCNN(
(backbone): FPN(
(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(top_block): LastLevelMaxPool()
(bottom_up): ResNet(
  (stem): BasicStem(
    (conv1): Conv2d(
      3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
      (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
    )
  )
  (res2): Sequential(
    (0): BottleneckBlock(
      (shortcut): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv1): Conv2d(
        64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv2d(
        256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
    )
    (2): BottleneckBlock(
      (conv1): Conv2d(
        256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
      )
      (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
    )
  )
  (res3): Sequential(
    (0): BottleneckBlock(
      (shortcut): Conv2d(
        256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv1): Conv2d(
        256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
    )
    (2): BottleneckBlock(
      (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
    )
    (3): BottleneckBlock(
      (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
      )
      (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
    )
  )
  (res4): Sequential(
    (0): BottleneckBlock(
      (shortcut): Conv2d(
        512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
      (conv1): Conv2d(
        512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
    (2): BottleneckBlock(
      (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
    (3): BottleneckBlock(
      (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
    (4): BottleneckBlock(
      (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
    (5): BottleneckBlock(
      (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
      )
      (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
      )
    )
  )
  (res5): Sequential(
    (0): BottleneckBlock(
      (shortcut): Conv2d(
        1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
      )
      (conv1): Conv2d(
        1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv2d(
        2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
      )
    )
    (2): BottleneckBlock(
      (conv1): Conv2d(
        2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
      )
      (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
      )
    )
  )
)
)
(proposal_generator): RPN(
(rpn_head): StandardRPNHead(
  (conv): Conv2d(
    256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
    (activation): ReLU()
  )
  (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
  (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
(anchor_generator): DefaultAnchorGenerator(
  (cell_anchors): BufferList()
)
)
(roi_heads): StandardROIHeads(
(box_pooler): ROIPooler(
  (level_poolers): ModuleList(
    (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
    (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
    (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
    (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
  )
)
(box_head): FastRCNNConvFCHead(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (fc1): Linear(in_features=12544, out_features=1024, bias=True)
  (fc_relu1): ReLU()
  (fc2): Linear(in_features=1024, out_features=1024, bias=True)
  (fc_relu2): ReLU()
)
(box_predictor): FastRCNNOutputLayers(
  (cls_score): Linear(in_features=1024, out_features=2, bias=True)
  (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)
)
)
)
[04/17 13:20:38 d2.data.datasets.coco]: Loaded 2952 images in COCO format from /Users/andreivicol/Doctorat/An1/YOLO/scripts/dataset/input/pTrain_masks/train.json
[04/17 13:20:38 d2.data.build]: Removed 8 images with no usable annotations. 2944 images left.
[04/17 13:20:38 d2.data.build]: Distribution of instances among all 1 categories:
|  category  | #instances   |
|:----------:|:-------------|
|  tobacco   | 12463        |
|            |              |
[04/17 13:20:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[04/17 13:20:38 d2.data.build]: Using training sampler TrainingSampler
[04/17 13:20:38 d2.data.common]: Serializing the dataset using: <class 'detectron2.data.common._TorchSerializedList'>
[04/17 13:20:38 d2.data.common]: Serializing 2944 elements to byte tensors and concatenating them all ...
[04/17 13:20:38 d2.data.common]: Serialized dataset takes 3.89 MiB
[04/17 13:20:38 d2.data.build]: Making batched data loader with batch_size=4
[04/17 13:20:38 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl ...
Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 1024) in the checkpoint but (2, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (2,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 1024) in the checkpoint but (4, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (4,) in the model! You might want to double check if this is expected.
Some model parameters or buffers are not found in the checkpoint:
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
[04/17 13:20:38 d2.engine.train_loop]: Starting training from iteration 0
/Users/andreivicol/miniconda3/envs/detectron2/lib/python3.12/site-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1711403226120/work/aten/src/ATen/native/TensorShape.cpp:3550.)
return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

[04/17 13:23:04 d2.utils.events]: eta: 7 days, 6:13:20 iter: 19 total_loss: 1.87 loss_cls: 0.7303 loss_box_reg: 0.8469 loss_rpn_cls: 0.2471 loss_rpn_loc: 0.03693 time: 7.0937 last_time: 6.1614 data_time: 0.1178 last_data_time: 0.0017 lr: 3.8962e-06
[04/17 13:25:31 d2.utils.events]: eta: 7 days, 22:27:45 iter: 39 total_loss: 1.832 loss_cls: 0.6939 loss_box_reg: 0.8942 loss_rpn_cls: 0.2154 loss_rpn_loc: 0.03777 time: 7.2422 last_time: 8.3476 data_time: 0.0015 last_data_time: 0.0014 lr: 7.8922e-06


## Expected behavior:

I would expect training to take a significantly shorter time. I'm getting an estimate of 7 days.

## Environment:

Paste the output of the following command:

Name Version Build Channel

absl-py 2.1.0 pypi_0 pypi antlr4-python3-runtime 4.9.3 pypi_0 pypi black 24.4.0 pypi_0 pypi blas 1.0 openblas
brotli-python 1.0.9 py312h313beb8_7
bzip2 1.0.8 h80987f9_5
ca-certificates 2024.3.11 hca03da5_0
certifi 2024.2.2 py312hca03da5_0
charset-normalizer 2.0.4 pyhd3eb1b0_0
click 8.1.7 pypi_0 pypi cloudpickle 3.0.0 pypi_0 pypi contourpy 1.2.1 pypi_0 pypi cycler 0.12.1 pypi_0 pypi detectron2 0.6 pypi_0 pypi expat 2.5.0 h313beb8_0
filelock 3.13.1 py312hca03da5_0
fonttools 4.51.0 pypi_0 pypi freetype 2.12.1 h1192e45_0
fvcore 0.1.5.post20221221 pypi_0 pypi grpcio 1.62.1 pypi_0 pypi hydra-core 1.3.2 pypi_0 pypi idna 3.4 py312hca03da5_0
iopath 0.1.9 pypi_0 pypi jinja2 3.1.3 py312hca03da5_0
jpeg 9e h80987f9_1
kiwisolver 1.4.5 pypi_0 pypi lcms2 2.12 hba8e193_0
lerc 3.0 hc377ac9_0
libcxx 14.0.6 h848a8c0_0
libdeflate 1.17 h80987f9_1
libffi 3.4.4 hca03da5_0
libgfortran 5.0.0 11_3_0_hca03da5_28
libgfortran5 11.3.0 h009349e_28
libjpeg-turbo 2.0.0 h1a28f6b_0
libopenblas 0.3.21 h269037a_0
libpng 1.6.39 h80987f9_0
libtiff 4.5.1 h313beb8_0
libwebp-base 1.3.2 h80987f9_0
llvm-openmp 14.0.6 hc6e5704_0
lz4-c 1.9.4 h313beb8_0
markdown 3.6 pypi_0 pypi markupsafe 2.1.3 py312h80987f9_0
matplotlib 3.8.4 pypi_0 pypi mpmath 1.3.0 py312hca03da5_0
mypy-extensions 1.0.0 pypi_0 pypi ncurses 6.4 h313beb8_0
networkx 3.1 py312hca03da5_0
numpy 1.26.4 py312h7f4fdc5_0
numpy-base 1.26.4 py312he047099_0
omegaconf 2.3.0 pypi_0 pypi opencv-python 4.9.0.80 pypi_0 pypi openjpeg 2.3.0 h7a6adac_2
openssl 3.0.13 h1a28f6b_0
packaging 24.0 pypi_0 pypi pathspec 0.12.1 pypi_0 pypi pillow 10.2.0 py312h80987f9_0
pip 23.3.1 py312hca03da5_0
platformdirs 4.2.0 pypi_0 pypi portalocker 2.8.2 pypi_0 pypi protobuf 5.26.1 pypi_0 pypi pycocotools 2.0.7 pypi_0 pypi pyparsing 3.1.2 pypi_0 pypi pysocks 1.7.1 py312hca03da5_0
python 3.12.2 h99e199e_0
python-dateutil 2.9.0.post0 pypi_0 pypi pytorch 2.2.2 py3.12_0 pytorch pyyaml 6.0.1 py312h80987f9_0
readline 8.2 h1a28f6b_0
requests 2.31.0 py312hca03da5_1
setuptools 68.2.2 py312hca03da5_0
six 1.16.0 pypi_0 pypi sqlite 3.41.2 h80987f9_0
sympy 1.12 py312hca03da5_0
tabulate 0.9.0 pypi_0 pypi tensorboard 2.16.2 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi termcolor 2.4.0 pypi_0 pypi tk 8.6.12 hb8d0fd4_0
torchaudio 2.2.2 py312_cpu pytorch torchvision 0.17.2 py312_cpu pytorch tqdm 4.66.2 pypi_0 pypi typing_extensions 4.9.0 py312hca03da5_1
tzdata 2024a h04d1e81_0
urllib3 2.1.0 py312hca03da5_1
werkzeug 3.0.2 pypi_0 pypi wheel 0.41.2 py312hca03da5_0
xz 5.4.6 h80987f9_0
yacs 0.1.8 pypi_0 pypi yaml 0.2.5 h1a28f6b_0
zlib 1.2.13 h5a0b063_0
zstd 1.5.5 hd90d995_0

github-actions[bot] commented 2 months ago

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github-actions[bot] commented 2 months ago

Requested information was not provided in 7 days, so we're closing this issue.

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facebookresearch / detectron2

Very slow training on Apple M1 Pro #5260

Name Version Build Channel