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Experimental reinforcement learning.
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@ -542,6 +542,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
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char *mapf = option_find_str(options, "map", 0);
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int *map = 0;
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if (mapf) map = read_map(mapf);
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FILE* reinforcement_fd = NULL;
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network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
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if (weightfile) {
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@ -723,6 +724,15 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
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unique_truth_count += num_labels;
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//static int previous_errors = 0;
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//int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
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//int errors_in_this_image = total_errors - previous_errors;
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//previous_errors = total_errors;
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//if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
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//char buff[1000];
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//sprintf(buff, "%s\n", path);
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//if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);
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free_detections(dets, nboxes);
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free(id);
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free_image(val[t]);
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@ -843,6 +853,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
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free(truth_classes_count);
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fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
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if (reinforcement_fd != NULL) fclose(reinforcement_fd);
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}
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#ifdef OPENCV
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