<yapılandırma_dosyası> Modelimizin yapılandırma dosyasının tam adı
training klasörüne attığımız yapılandırma dosyaları
Örn: ssd_inception_v2_coco.config
Eskimiş olan bir eğitim kodudur, model_main.py kod dosyası tensorflow tarafından önerilir.
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:Restoring parameters from ssd_inception_v2_coco/model.ckpt
INFO:tensorflow:Running local_init_op.INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting Session.
INFO:tensorflow:Saving checkpoint to path training\model.ckpt
INFO:tensorflow:Starting Queues.
INFO:tensorflow:global_step/sec: 0INFO:tensorflow:global step 1: loss = 13.8886
INFO:tensorflow:global step 2: loss = 16.2202 (0.937 sec/step)
INFO:tensorflow:global step 3: loss = 13.7876 (0.904 sec/step)
INFO:tensorflow:global step 4: loss = 12.9230 (0.894 sec/step)
INFO:tensorflow:global step 5: loss = 12.7497 (0.922 sec/step)
INFO:tensorflow:global step 6: loss = 11.7563 (0.936 sec/step)
INFO:tensorflow:global step 7: loss = 11.7245 (0.910 sec/step)
INFO:tensorflow:global step 8: loss = 10.7993 (0.916 sec/step)
INFO:tensorflow:global step 9: loss = 9.1277 (0.890 sec/step)
INFO:tensorflow:global step 10: loss = 9.3972 (0.919 sec/step)
INFO:tensorflow:global step 11: loss = 9.9487 (0.897 sec/step)
INFO:tensorflow:global step 12: loss = 8.7954 (0.884 sec/step)
INFO:tensorflow:global step 13: loss = 7.4329 (0.906 sec/step)
INFO:tensorflow:global step 14: loss = 7.8270 (0.897 sec/step)
INFO:tensorflow:global step 15: loss = 6.4877 (0.894 sec/step)
🧲 Eğitimi Etkileyen Faktörler
Training times can be affected by a number of factors such as:
The computational power of you hardware (either CPU or GPU): Obviously, the more powerful your PC is, the faster the training process.
Whether you are using the TensorFlow CPU or GPU variant: In general, even when compared to the best CPUs, almost any GPU graphics card will yield much faster training and detection speeds. As a matter of fact, when I first started I was running TensorFlow on my Intel i7-5930k (6/12 cores @ 4GHz, 32GB RAM) and was getting step times of around 12 sec/step, after which I installed TensorFlow GPU and training the very same model -using the same dataset and config files- on a EVGA GTX-770 (1536 CUDA-cores @ 1GHz, 2GB VRAM) I was down to 0.9 sec/step!!! A 12-fold increase in speed, using a “low/mid-end” graphics card, when compared to a “mid/high-end” CPU.
How big the dataset is: The higher the number of images in your dataset, the longer it will take for the model to reach satisfactory levels of detection performance.
The complexity of the objects you are trying to detect: Obviously, if your objective is to track a black ball over a white background, the model will converge to satisfactory levels of detection pretty quickly. If on the other hand, for example, you wish to detect ships in ports, using Pan-Tilt-Zoom cameras, then training will be a much more challenging and time-consuming process, due to the high variability of the shape and size of ships, combined with a highly dynamic background.
And many, many, many, more. . . .
👀 Eğitim İşlemini TensorBoard Kullanarak Takip Etme
Anaconda Prompt üzerinden alttaki komutlar uygulanır:
activate tensorflow_cpu # ya da gputensorboard --logdir=training\
Alttaki gibi bir çıktı gelmesi gerekmekte:
TensorBoard 1.6.0 at http://YOUR-PC:6006 (Press CTRL+C to quit)
Çıktıyı görüntülemek için verilen url'i tarayıcına kopyalaman yeterlidir.
📃 Sonuç Grafiğini Dışarı Aktarma
Anaconda Prompt üzerinden alttaki komutlar uygulanır:
activate tensorflow_cpu # ya da gpucopy
%TENSORFLOW%\models\research\object_detection/export_inference_graph.py \
%TENSORFLOW%\workspace\example_detection
cd %TENSORFLOW%\workspace\example_detection
python export_inference_graph.py \
--input_type image_tensor \
--pipeline_config_path training/<yapılandırma_dosyası> \
--trained_checkpoint_prefix training/model.ckpt-<checkpoint> \
--output_directory trained-inference-graphs/output_inference_graph_v1.pb
<yapılandırma_dosyası> Modelimizin yapılandırma dosyasının tam adı
training klasörüne attığımız yapılandırma dosyaları
