raccoon_dataset
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The dataset is used to train my own raccoon detector and I blogged about it on Medium
Hello guys, I need help. Since I use generate_tfrecord.py to generate TFrecord files, I've encountered with many issues. 
i have to classes objects of army tank. 1.merkava 2. chieftain but Wrong in detection, So that it detection all the images either merkava or chieftain, While the Merkava tank...
env: windows7 conda : 4.5.4 I execute the following command, it will fail and jump out "TypeError". $ python generate_tfrecord.py --csv_input=clothes_labels.csv --output_path=clothes_test.record /*====================================*/ Traceback (most recent call last): File "generate_tfrecord.py",...
When using my own images to train for object identification with tensorflow. I have followed all instructions given by: https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10 As im using the command: "python generate_tfrecord.py --csv_imput=images\train_labels.csv --image_dir=images\train --output_path=train.record"...
What version of Tensorflow did you work on?
: Cannot take a larger sample than population when 'replace=False'
In the dictory of images, all of them are .jpg images, but why they are turned to .png images written in xml files?? looking for your awser, Regards!
While running Generat_tfrecord.py, it says some images are not found in the directory even I using raccoon images and the same steps done by datitran. Pls help me to solve...
when I run "python generate_tfrecord.py --csv_input=sunglasses_verfication_labels.csv --output_path=sunglass_verfication.record ", error as follow: Traceback (most recent call last): File "generate_tfrecord.py", line 99, in tf.app.run() File "C:\Users\DSL\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 126, in run _sys.exit(main(argv)) File...
A well-trained raccoon model only recognizes raccoons. How do you identify more objects with ssd_mobilenet_v1_coco? Load two models at the same time? Is there a better way