jimgoo/caffe-oxford102

Caffe CNNs for the Oxford 102 flower dataset

Hi there 👋

I do open source research with LAION and Stability AI MedARC on reconstructing complex images from fMRI brain activity using diffusion models.

caffe-oxford102's Issues

can you tell me ?

I use the same way as you and obtained a good performance,but when I use a single picture to test the model it only obtained 0.0098 accuracy.....please tell me why？

Training Dataset Mean and RGB or BGR?

Thanks a lot for great work!
Please could you let me know what mean numbers and channel order I should use to pre-process images?

How did you normalize the images before training?

I am trying to implement the same project but using Tensorflow, and I was surprised when you were able to achieve 80% accuracy after only 500 iterations. May I ask how did you normalize the images before training? Did you divide them by 255 or subtract the ImageNet mean?
Thank you

[Question] Could you tell me how to use /oxford102.caffemodel?

Hi.

Since http://zeus.robots.ox.ac.uk/flower_demo/demo currently doesn't work, I am trying your model.

I wrote the following script, but it outputs almost same percentage (about 1%) probabilities of all labels even if the input is the one in the data set.

Could you tell me my misunderstanding to use your model?

Thanks,

import numpy as np
import caffe

classifier = caffe.Classifier("<git folder>/caffe-oxford102/AlexNet/deploy.prototxt", "oxford102.caffemodel")

image = caffe.io.load_image("<git folder>/caffe-oxford102/data/jpg/image_00348.jpg")
inputs = [image]

predictions = classifier.predict(inputs, oversample=False)
print(predictions)

How splits.png was generated?

Hi,
may I ask that how the splits.png was generated?
Thanks!

IndexError: too many indices for array

VGG_S finetune

Hi,
Thanks for sharing the experiments info, I was wondering that if could give me a link to pretrained-weight for VGG_S finetune part?

AlexNet/pretrained-weights.caffemodel AccessDenied

Thanks for the nice tutorial! However, the AlexNet model cannot be downloaded -- https://s3.amazonaws.com/jgoode/cannaid/bvlc_reference_caffenet.caffemodel returns "forbidden" or "access denied".

About dataset

Hi, How can I get my own dataset format, like the imagelabels.mat and setid.mat if I want train my own models, Thx

so, you just used the training set(1020 images) to fine tuned the AlexNet and got a good result?

I fine tuned the AlexNet with the training set, and the loss divergent, but when using the test set(6k images) instead, it converged. So I'm really curious about how you fine tuned the AlexNet and got a good result. : )

holle， please tell me the difference between test_acc and test_acc_flipped?? thank you!!

holle， please tell me the difference between test_acc and test_acc_flipped?? thank you!!

type: IMAGE_DATA ？

the layer is:

layers {
  name: "data"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    #source: "/home/ubuntu/git/caffe-oxford102/train.txt"
        source: "/home/ubuntu/git/caffe-oxford102/test.txt" # Flipped
    batch_size: 50
    new_height: 256
    new_width: 256
  }
  transform_param {
    crop_size: 227
    mean_file: "/home/ubuntu/caffe/data/ilsvrc12/imagenet_mean.binaryproto"
    mirror: true
  }
  include: { phase: TRAIN }
}

erro is:
Unknown layer type: ImageData (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultinomialLogisticLoss, PReLU, Parameter, Pooling, Power, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile)