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Tackles bryant1410/readmesfix#1

I used one class cfg and .weights file like this: ./darknet yolo test cfg/yolo1-tiny-obj.cfg yolo1-tiny-obj_40000.weights. Then error happened in [detection] layer: 15: darknet: ./src/detection_layer.c:25: make_detection_layer: Assertion `sideside((1 + l.coords)*l.n + l.classes) == inputs' failed. 已放弃 (核心已转储) My cfg file like this: [net] batch=64 subdivisions=8 height=448 width=448 channels=3 momentum=0.9 decay=0.0005

saturation=.75 exposure=.75 hue = .1

learning_rate=0.0005 policy=steps steps=200,400,600,800,20000,30000 scales=2.5,2,2,2,.1,.1 max_batches = 40000

[convolutional] batch_normalize=1 filters=16 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky

[connected] output= 1470 activation=linear

[detection] classes=1 coords=4 rescore=1 side=7 num=2 softmax=0 sqrt=1 jitter=.2

object_scale=1 noobject_scale=.5 class_scale=1 coord_scale=5

Have anyone known how to solve the problem? Thank you.

for i in range(len(boxes_filtered)):
			if probs_filtered[i] == 0 : continue
			for j in range(i+1,len(boxes_filtered)):
				if self.iou(boxes_filtered[i],boxes_filtered[j]) > self.iou_threshold : 
					probs_filtered[j] = 0.0
		
		filter_iou = np.array(probs_filtered>0.0,dtype='bool')
		boxes_filtered = boxes_filtered[filter_iou]
		probs_filtered = probs_filtered[filter_iou]
		classes_num_filtered = classes_num_filtered[filter_iou]

This code indicates that you remove the high iou, but I think you should remove the low.

the order of import cause different cv2.imread() result, I don't know reason. In current order, cv2.imread() returns None. But after change the order of "import tensorflow as tf" and "import cv2", the function works correctly.

I tried to write a script to extract weight values from darknet's file.But I failed.The darknet's yolo-tiny.weight file is 172M.While I extracted weights referring to the structure of the yolo-tiny network ,I only got 123M's weights.Anything I missed? Is there something else in the yolo-tiny.weight file? Any instruction is appreciated! PS:Thank you for your great work!

1. Add count classes function
2. cv2.imwrite saved check
3. add -fromfolder function, user can specify which folder to read pictures from and program will return overall accuracy

When I conducted sudo ./darknet yolo test ./cfg/yolo-tiny.cfg ./yolo-tiny.weights to get yolo-tiny.ckpt. Error was ./darknet：找不到命令? Have you run into? Thank you.

Good morning! I have trained my own model with the darknet framework from the official web and I get the .weights file as the result. I want to know how to tranform the .weights file into .ckpt file and to use your program to detect pictures. Thanks a lot!

When trying to load YOLO_small.ckpt, at line self.fc_32 = self.fc_layer(32,self.fc_30,1470,flat=False,linear=True) I get the error:

modprobe: ERROR: could not insert 'nvidia_378_uvm': Invalid argument
E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit: CUDA_ERROR_UNKNOWN
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: vm-gpu
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: vm-gpu
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: 367.57.0
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module  367.57  Mon Oct  3 20:37:01 PDT 2016
GCC version:  gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 367.57.0
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:300] kernel version seems to match DSO: 367.57.0

Is the driver version hard coded into the checkpoint file? My driver is 367.57.0, as you can tell. I can run other tensorflow code fine. Using tensorflow-gpu=0.12.1. Using the same code on an exact same machine with a newer drive is fine, but this machine cannot have its drivers updated for reasons, so I'm wondering if it's possible to run this file with the older driver. I use CUDA 8.0 and cuDNN 5.1

When I run python YOLO_small_tf.py -fromfile /Users/yashajain/Downloads/yolotf-master/test/person.jpg

It builds the network , but fails with :

ValueError: Restore called with invalid save path: 'weights/YOLO_small.ckpt'. File path is: 'weights/YOLO_small.ckpt'

I have the YOLO_small.ckpt file in weights/YOLO_small.ckpt'

First, my sincere thanks for making the implementation available in tensorflow framework.

Problem 1: When I run on "small" weight file, I sometimes face the issue sometimes not even for the same image as present in the test folder. The issue is "OverflowError: signed integer is less than minimum"

My PC specifics have a CPU RAM of 12gb and a GPU TITAN X

Problem 2: When I run on "tiny" weight file, the image after detection shows rectangle made on different parts even when testing multiple times on the same image. Also, the the custom setting seem not to work in my case.

Please do help. Thanks.

@gliese581gg hey, I want to extract weights of yolov2-tiny-voc.weights. But this code supports only yolo1 right ? How can I extract weights of tiny yolov2 ? please guide me. Thanks in advance!

I followed the weight_extractor doc for converting yolov2-tiny.cfg and yolov2-tiny.weights using the following command

./darknet yolo test cfg/yolov2-tiny.cfg yolov2-tiny.weights

However, there were no text file written to cjy folder. The output was this : First section must be [net] or [network]: Success

Any ideas on this ? @gliese581gg

I think that the 'classes_num_filtered' variant should be derived from 'probs', not 'filter_mat_probs' in interpret_output function.Could you tell me if it is the right thing to do?

I've trained yolov3 on mobilenet backend and get a good MAP(about 90M and 2 times faster than keras yolov3).I upload my code for mobilenet-yolov3 on https://github.com/Adamdad/keras-YOLOv3-mobilenet.git use it as you wish

run: python YOLO_tiny_tf.py -test.jpg error shows up as follow:

C:\Users\Max\Anaconda3\envs\tensorflow\python.exe E:/condaDev/YOLO_tensorflow-master/YOLO_tiny_tf.py Building YOLO_tiny graph... Layer 1 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 16, Input channels = 3 Layer 2 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 3 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 32, Input channels = 16 Layer 4 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 5 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 64, Input channels = 32 Layer 6 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 7 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 128, Input channels = 64 Layer 8 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 9 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 256, Input channels = 128 Layer 10 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 11 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256 Layer 12 : Type = Pool, Size = 2 * 2, Stride = 2 Layer 13 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 512 Layer 14 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 1024 Layer 15 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 1024 Layer 16 : Type = Full, Hidden = 256, Input dimension = 50176, Flat = 1, Activation = 1 Layer 17 : Type = Full, Hidden = 4096, Input dimension = 256, Flat = 0, Activation = 1 Layer 19 : Type = Full, Hidden = 1470, Input dimension = 4096, Flat = 0, Activation = 0 2018-08-24 13:26:27.092913: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 WARNING:tensorflow:From C:\Users\Max\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Use tf.global_variables_initializer instead. Loading complete!

seems run partly, outputs some info, but the rest part disappeared