Minor update to variable names in Example 7
"data_specs = [DataSpec(type_='IMAGE', layer='Input1', data=inputVars),\n",
" DataSpec(type_='OBJECTDETECTION', layer='Detection1', data=targets)]\n",
"gpu = Gpu(devices=[0])\n",
- "model.model_weights = s.CASTable('tinyyolov2_40epoch_trainless100')\n",
- "model.fit(data='trainset', \n",
+ "yolo_model.model_weights = s.CASTable('tinyyolov2_40epoch_trainless100')\n",
+ "yolo_model.fit(data='trainset', \n",
" optimizer=optimizer, \n",
" data_specs=data_specs, \n",
" n_threads=1, \n",
" record_seed=13309,\n",
" force_equal_padding=True, \n",
- " gpu= gpu\n",
- " )"
+ " gpu=gpu)"
]
},
{
}
],
"source": [
- "model.predict(data=predict_tbl, gpu = Gpu(devices=[0]))"
+ "yolo_model.predict(data=predict_tbl, gpu = Gpu(devices=[0]))"
]
},
{
"display_object_detections(conn=s, \n",
" coord_type='yolo', \n",
" max_objects=5, \n",
- " table=model.valid_res_tbl, \n",
+ " table=yolo_model.valid_res_tbl, \n",
" num_plot=10,\n",
" n_col=3)"
]
}
],
"source": [
- "model.predict(data='trainset', gpu=Gpu(devices=[0]), buffer_size=1)"
+ "yolo_model.predict(data='trainset', gpu=Gpu(devices=[0]), buffer_size=1)"
]
},
{
}
],
"source": [
- "metrics = model.evaluate_object_detection(ground_truth='trainset', coord_type='yolo',\n",
+ "metrics = yolo_model.evaluate_object_detection(ground_truth='trainset', coord_type='yolo',\n",
" classes=['Airplane', 'Vehicle'], \n",
" iou_thresholds=0.5)"
]
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.5"
+ "version": "3.5.5"
}
},
"nbformat": 4,
