pycaffe

Accuracy reported by caffe and pycaffe are different

由 人盡茶涼 提交于 2019-11-28 02:19:05
Below is the train.Prototxt file that is used to train a pretrained model. name: "TempWLDNET" layer { name: "data" type: "ImageData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 224 mean_file: "mean.binaryproto" } image_data_param { source: "train.txt" batch_size: 25 new_height: 256 new_width: 256 } } layer { name: "data" type: "ImageData" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 224 mean_file: "painmean.binaryproto" } image_data_param { source: "test.txt" batch_size: 25 new_height: 256 new_width:

Backward pass in Caffe Python Layer is not called/working?

由 我只是一个虾纸丫 提交于 2019-11-27 22:41:59
问题 I am unsuccessfully trying to implement a simple loss layer in Python using Caffe. As reference, I found several layers implemented in Python, including here, here and here. Starting with the EuclideanLossLayer as provided by the Caffe documentation/examples, I was not able to get it working and startd debugging. Even using this simple TestLayer : def setup(self, bottom, top): """ Checks the correct number of bottom inputs. :param bottom: bottom inputs :type bottom: [numpy.ndarray] :param top

Multiple category classification in Caffe

由 微笑、不失礼 提交于 2019-11-27 20:41:31
I thought we might be able to compile a Caffeinated description of some methods of performing multiple category classification . By multi category classification I mean: The input data containing representations of multiple model output categories and/or simply being classifiable under multiple model output categories. E.g. An image containing a cat & dog would output (ideally) ~1 for both the cat & dog prediction categories and ~0 for all others. Based on this paper , this stale and closed PR and this open PR , it seems caffe is perfectly capable of accepting labels. Is this correct? Would

NameError: name 'get_ipython' is not defined

由 北战南征 提交于 2019-11-27 12:48:23
问题 I am working on Caffe framework and using PyCaffe interface. I am using a Python script obtained from converting the IPython Notebook 00-classification.ipynb for testing the classification by a trained model for ImageNet. But any get_ipython() statement in the script is giving the following error: $ python python/my_test_imagenet.py Traceback (most recent call last): File "python/my_test_imagenet.py", line 23, in <module> get_ipython().magic(u'matplotlib inline') In the script, I'm importing

Cheat sheet for caffe / pycaffe?

由 非 Y 不嫁゛ 提交于 2019-11-27 04:58:49
问题 Does anyone know whether there is a cheat sheet for all important pycaffe commands? I was so far using caffe only via Matlab interface and terminal + bash scripts. I wanted to shift towards using ipython and work through the ipython notebook examples. However I find it hard to get an overview of all the functions that are inside the caffe module for python. (I'm also quite new to python). 回答1: The pycaffe tests and this file are the main gateway to the python coding interface. First of all,

caffe data layer example step by step

由 Deadly 提交于 2019-11-27 04:53:00
I want to find a caffe python data layer example to learn. I know that Fast-RCNN has a python data layer, but it's rather complicated since I am not familiar with object detection. So my question is, is there a python data layer example where I can learn how to define my own data preparation procedure? For example, how to do define a python data layer do much more data augmentation (such as translation, rotation etc.) than caffe "ImageDataLayer" . Thank you very much You can use a "Python" layer: a layer implemented in python to feed data into your net. (See an example for adding a type:

Building custom Caffe layer in python

由 你离开我真会死。 提交于 2019-11-27 00:13:55
问题 After parsing many links regarding building Caffe layers in Python i still have difficulties in understanding few concepts. Can please someone clarify them? Blobs and weights python structure for network is explained here: Finding gradient of a Caffe conv-filter with regards to input. Network and Solver structure is explained here: Cheat sheet for caffe / pycaffe?. Example of defining python layer is here: pyloss.py on git. Layer tests here: test layer on git. Development of new layers for C+

Accuracy reported by caffe and pycaffe are different

由 拟墨画扇 提交于 2019-11-26 23:40:34
问题 Below is the train.Prototxt file that is used to train a pretrained model. name: "TempWLDNET" layer { name: "data" type: "ImageData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 224 mean_file: "mean.binaryproto" } image_data_param { source: "train.txt" batch_size: 25 new_height: 256 new_width: 256 } } layer { name: "data" type: "ImageData" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 224 mean_file:

Caffe shape mismatch error using pretrained VGG-16 model

由 元气小坏坏 提交于 2019-11-26 17:18:37
问题 I am using PyCaffe to implement a neural network inspired by the VGG 16 layer network. I want to use the pre-trained model available from their GitHub page. Generally this works by matching layer names. For my "fc6" layer I have the following definition in my train.prototxt file: layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096 } } Here is the prototxt file for the VGG-16 deploy architecture. Note that the "fc6" in their prototxt is

caffe data layer example step by step

由 走远了吗. 提交于 2019-11-26 12:46:16
问题 I want to find a caffe python data layer example to learn. I know that Fast-RCNN has a python data layer, but it\'s rather complicated since I am not familiar with object detection. So my question is, is there a python data layer example where I can learn how to define my own data preparation procedure? For example, how to do define a python data layer do much more data augmentation (such as translation, rotation etc.) than caffe \"ImageDataLayer\" . Thank you very much 回答1: You can use a
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