In this paper, we introduce a new recipe dataset MIRecipe (Multimedia-Instructional Recipe). It has both text and image data for every cooking step, while the conventional recipe datasets only contain final dish images, and/or images only for some of the steps. It consists of 26,725 recipes, which include 239,973 steps in total. The recognition of ingredients in images associated with cooking steps poses a new challenge: Since ingredients are processed during cooking, the appearance of the same ingredient is very different in the beginning and finishing stages of the cooking. The general object recognition methods, which assume the constant appearance of objects, do not perform well for such objects. To solve the problem, we propose two stage-aware techniques: stage-wise model learning, which trains a separate model for each stage, and stage-aware curriculum learning, which starts with the training data from the beginning stage and proceeds to the later stages. Our experiment with our dataset shows that our method achieves higher accuracy than the model trained using all the data without considering the stages. Our dataset is available at our GitHub repository.