Comments (1)
on my quad-core iMac with an SSD and 16GB RAM the training process takes about 10 hours for one of the small VOC2007 categories such as bus. I would take a look into the directories which are created during training, first check the number of exemplar that were successfully trained (look inside models folder), then once training finished see how many trainval/test images have been processed (look into the applied folder).
The implementation doesn't use multiple cores, you should concurrently run the script N times where you have N cores on a machine. This was designed to run across a clusters of {dozens, hundreds, thousands, or millions} of nodes.
Please stay tuned for a cleaned up API and models files! (or take a sneak peak at the v1 branch on github)
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Related Issues (20)
- double SVM update step at end of iterations HOT 1
- training should allow for max image dim HOT 1
- Reg. matlab lock HOT 1
- update DT icon as mean image
- error in esvm_compile HOT 10
- Calibration part in exemplar-svm HOT 7
- Issue regarding version of matlab compiler compatibilty
- Regarding display of exemplar svm HOT 1
- Error while running esvm_demo_apply .... can some1 pls fix this HOT 1
- HOGPicture speedup HOT 1
- Bug in esvm_detect HOT 3
- Replace HoG with other features HOT 13
- Segmentation annotations HOT 2
- How to close co-occurrence
- ESVM with HMAX Features HOT 1
- The corresponding c code
- Limit max_exemplars and choose them per random
- Error in esvm_detect>esvm_detectdriverBLOCK (line 266)
- Unable to create mex file for libsvmpredict.c
- Weird code in esvm_nms HOT 1
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