using tf.slim to finetuning a model to new task

1. Download the train dataset fisher data and pretrained model, uncompress the dataset in train folder.

2. run the command cd covert_pys;python covert_somedata_to_tfrecord.py --dataset_name=train --dataset_dir=. --nFold=4 to split the train dataset to Train and val in 4 folds. Then, in folder tfrecords, we get the fish_train_00000-of-nFold--00001.tfrecord and fish_validation_00000-of-nFold--00001.tfrecord.

3. run cd run_scripts; sh run.sh to finetuning some layers to fit the new task (8 classification task).After finetuning the model, run sh run_eval.sh to eval the model.

4. (if you want to finetuning the all layers)run run_all.sh and run_all_eval.sh train the all layers and eval the model.

PS: When you train or eval the model, make tfrecords include only one folder tfrecord.

5. In fish_inference.py, we make an inference with the finetuning model.

make RESTful api with your model

In flask_inference.py, we build an serving model with flask. It is too simple to get a image file path in your computer to make inference, the model is hold in your memory when the scrip is runing.

I deploy a image classification in demo page. Feel free to try.