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add visual search #51
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mschrimpf
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Jul 24, 2020
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| vs_model_param = {} | ||
| vs_model_param['tar_pool'] = target_model_pool | ||
| vs_model_param['stim_pool'] = stimuli_model_pool | ||
| vs_model_param['model_layers'] = visual_search_layer | ||
| vs_model_param['tar_size'] = target_img_size | ||
| vs_model_param['stim_size'] = stimuli_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.