Add a brief tutorial on transfer learning about rmldnn HOT 7 CLOSED

Hi @yashjain-99, yes, we should be adding a full tutorial on transfer learning for image classification soon.
But just for clarification, the json files available under resources contain network descriptions only, not model parameters. For transfer learning, you'd also need the corresponding h5 file from Keras/Tensorflow, which contains the actual model parameters. We will provide that soon as well.

However, I'm curious by what you meant by "it didn't provide any results or return any errors". Were you able to run rmldnn? If so, could you show your config file and what you got as output? Thanks

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About issue when using same network as given in documentation of MNIST classification but with modification(Attached NN).
rmldnn docker image version: rmldnn latest
Configuration file: config_training.json and config_test.json(attached)
input data file: https://www.kaggle.com/datasets/gpiosenka/100-bird-species
Command run to reproduce the error: sudo docker run -u $(id -u):$(id -g) -v ${PWD}:/home/ubuntu -w /home/ubuntu --rm
rocketml/rmldnn:latest rmldnn --config=config_training.json
Expected behavior: It should have ran and should have given low accuracy as model is not complex enough for this large data.
Screenshots: Attached
Desktop: Ubuntu(22.04) , Docker(20.10.12)
All files available in zip below. Kindly refer that and for dataset use above mentioned kaggle link.
Files.zip
If you require any additional information or files, please let me know.

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@yashjain-99 could you try the following configuration file? Please note these changes:

1. resize in data --> transforms to match the input layer in the network
2. NLL loss function because this is a classification problem. Dice loss in your current configuration works for image segmentation problems.

{
    "neural_network": {
        "outfile": "out.txt",
        "num_epochs": 40,
        "layers": "./net.json",
        "checkpoints": {
            "save": "./model/",
            "interval": 20
        },
        "data": {
            "input_type": "images",
            "target_type": "labels",
            "input_path":      "./train",
            "test_input_path": "./valid",
            "batch_size": 8,
            "grayscale": false,
            "preload": false,
            "transforms": [
                { "resize": [224, 224] }
            ]
        },
        "optimizer": {
            "type": "Adam",
            "learning_rate": 1e-4
        },
        "loss": {
            "function": "NLL"
        }
    }
}

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Yes, it worked. I will provide the accuracy when the training is finished because it is taking far too long, even though I reduced the number of labels from 400 to 100.
Thanks for help. @ssbotelh @adavanisanti
screenshot attached:

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An error came up during evaluation: Target -1 out of bound.(Screenshots attached)

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Hi @yashjain-99, this error is caused by a mistake in the dataset. Notice how the directory
BLACK & YELLOW BROADBILL has an extra space in the training set, but not in the test or validation sets. As a result, test samples from that category will not have a matching label, thus the invalid -1 target label.

Please rename this directory in the training set by removing the extra space, and retry your run. In future releases, I'll make sure this error happens early and with a clear error message.

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Yes thanks a lot @ssbotelh , It is working all fine now.

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