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mschrimpf
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Jul 24, 2020
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| brain_translated_pool[identifier] = model | ||
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| def MLSearchPool(target_img_size=28, search_image_size=224): | ||
| target_model_pool = BaseModelPool(input_size=target_img_size) |
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| layers = { | ||
| 'vgg-16': [f'block{i + 1}_pool' for i in range(3,5)], | ||
| } |
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I would create another vgg-16 BrainModel from the same underlying BaseModel that makes these exact commitments
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The issue is that the "target_image" size is just 28x28 pixels. So we will need to exclusively define which layer will work for a specific ML model. So isn't it much better that we commit these layers in a separate py file? i.e. which layer to use for which ML model?
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check score_model example on visual search benchmark.