Object detection in fast.ai v1

Bronzi88 · May 29, 2019, 12:34pm

Hi,

the models return three thinks:

[1, 24480, 3] -> [Batchsize, number of boxes, classes + background] -> classification results for each anchor
[1, 24480, 4] -> [Batchsize, number of boxes, box-coordinates refinements] -> how the anchors should be resized to match the objects in the image
Feature map shapes. You can ignore that for a start is more for debug purposes

For inference please take a look at the following colab notebook:
https://colab.research.google.com/drive/16un8u65D4oTWiWzpdjMU_cpnVasaQ72n

or this function in the repo:

github.com

ChristianMarzahl/ObjectDetection/blob/6fc3ca7ae2197fed05a47d6a726d46ac4ae41c11/helper/object_detection_helper.py#L172




def show_preds(img, bbox_pred, preds, scores, classes, figsize=(5,5)):


    _, ax = plt.subplots(1, 1, figsize=figsize)
    for bbox, c, scr in zip(bbox_pred, preds, scores):
        img.show(ax=ax)
        txt = str(c.item()) if classes is None else classes[c.item()+1]
        draw_rect(ax, [bbox[1],bbox[0],bbox[3],bbox[2]], text=f'{txt} {scr:.2f}')




def show_results_side_by_side(learn: Learner, anchors, detect_thresh:float=0.2, nms_thresh: float=0.3,  image_count: int=5):


    with torch.no_grad():
        img_batch, target_batch = learn.data.one_batch(DatasetType.Valid, False, False, False)


        prediction_batch = learn.model(img_batch[:image_count])
        class_pred_batch, bbox_pred_batch = prediction_batch[:2]


        bbox_gt_batch, class_gt_batch = target_batch[0][:image_count], target_batch[1][:image_count]


        for img, bbox_gt, class_gt, clas_pred, bbox_pred in list(