Training with new database about ademxapp HOT 7 CLOSED

Thanks. I will make this part clearer, with examples.

python issegm/voc.py --gpus 0,1,2,3 --split train --data-root ${New_database} --output output --model ${New_database}_rna-a1_cls${Number_of_classes} --batch-images 16 --crop-size 500 --origin-size 2048 --scale-rate-range 0.7,1.3 --weights models/ilsvrc-cls_rna-a1_cls1000_ep-0001.params --lr-type fixed --base-lr 0.0016 --to-epoch 140 --kvstore local --prefetch-threads 4 --prefetcher thread --backward-do-mirror

Changing --crop-size and/or --origin-size as you like is supposed to be OK.
Besides,

1. prepare split files and save them into issegm/data/${New_database};
2. specify --cache-images 0 if your new data are too large to hold in host memory.

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@itijyou Thank you for the reply.

May I ask more question?

1. dataset definition?

There is a part like , line number 250 in voc.py
if dataset == 'ade20k': num_classes = model_specs.get('classes', 150) label_2_id = np.arange(-1, 150) label_2_id[0] = 255 id_2_label = np.arange(1, 256+1) id_2_label[255] = 0 valid_labels = range(1, 150+1) # if args.split == 'test': cmap_path = None # max_shape = np.array((2100, 2100)) if model_specs.get('balanced', False) and args.split == 'trainval': meta['image_classes']['trainval'] = meta['image_classes']['train'] + meta['image_classes']['val']

I think I need to define something here for a new dataset. I dont know what I have to do.

2. A format of label

A format of label is different for each dataset. So what format is allowed? (e.g. (wxh and intensity=label) or (wxhxc intensity =[label, label, label])

Thank you!

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@DonghyunK

1. Yes. I thought there were a default setting in this function (but apparently not).
   In most cases, adding something like this should suffice.

elif dataset == 'pascal-context':
        num_classes = model_specs.get('classes', 60)
        valid_labels = range(num_classes)
        #
        max_shape = np.array((500, 500))

2. wxh and intensity=label

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@itijyou

Thank you, I can train it now.

Could you please tell me how I can check an accuracy on a validation set for each epoch?

MXNet is totally new to me, so that I am sorry to ask you many questions.

Thanks.

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Specify the `eval_data' parameter when calling mod.fit

For MXNet related questions, it'd be better to ask at the MXNet project.

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@itijyou

I appreciate and thank you for the answers.

Since I resolved this issue, I close this issue.

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Thanks. I will make this part clearer, with examples.

python issegm/voc.py --gpus 0,1,2,3 --split train --data-root ${New_database} --output output --model ${New_database}_rna-a1_cls${Number_of_classes} --batch-images 16 --crop-size 500 --origin-size 2048 --scale-rate-range 0.7,1.3 --weights models/ilsvrc-cls_rna-a1_cls1000_ep-0001.params --lr-type fixed --base-lr 0.0016 --to-epoch 140 --kvstore local --prefetch-threads 4 --prefetcher thread --backward-do-mirror

Changing --crop-size and/or --origin-size as you like is supposed to be OK.
Besides,

1. prepare split files and save them into issegm/data/${New_database};
2. specify --cache-images 0 if your new data are too large to hold in host memory.

hello，i follow this command to train the model on the pascal voc dataset, but when I test the model after training ,the prediction is all black. do you know how to deal with it??
Thanks.

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