created export.pkl file

.gitignore

@@ -1,4 +1,7 @@
-*.pkl
+#*.pkl
+data*/
+
+
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

model_training.py

@@ -0,0 +1,69 @@
+from fastbook import *
+from fastai.vision.widgets import *
+
+## Gathering Data
+
+path = Path('data_devices')
+
+device_types = 'laptop','desktop pc','mobile phone'
+
+if not path.exists():
+    path.mkdir()
+    for o in device_types:
+        dest = (path/o)
+        dest.mkdir(exist_ok=True)
+        results = search_images_ddg(f'{o}', max_images=100)
+        # results = search_images_bing(key, f'{o} bear')
+        # download_images(dest, urls=results.attrgot('contentUrl'))
+        download_images(dest, urls=results)
+fns = get_image_files(path)
+
+
+failed = verify_images(fns)
+
+
+failed.map(Path.unlink);
+
+#* 
+## From Data to DataLoaders
+devices = DataBlock(
+    blocks=(ImageBlock, CategoryBlock), 
+    get_items=get_image_files, 
+    splitter=RandomSplitter(valid_pct=0.2, seed=42),
+    get_y=parent_label,
+    item_tfms=Resize(128))
+dls = devices.dataloaders(path)
+
+
+
+
+
+### Data Augmentation
+
+
+## Training Your Model, and Using It to Clean Your Data
+devices = devices.new(
+    item_tfms=RandomResizedCrop(224, min_scale=0.5),
+    batch_tfms=aug_transforms())
+dls = devices.dataloaders(path)
+learn = vision_learner(dls, resnet18, metrics=error_rate)
+learn.fine_tune(4)
+interp = ClassificationInterpretation.from_learner(learn)
+interp.plot_confusion_matrix()
+
+
+
+
+## Turning Your Model into an Online Application
+
+### Using the Model for Inference
+learn.export()
+path = Path()
+path.ls(file_exts='.pkl')
+learn_inf = load_learner(path/'export.pkl')
+query02 = "dell inspiron"
+urls = search_images_ddg(query02, max_images = 5 )
+dest = f'data_test/{query02}.jpg'
+download_url(urls[0], dest)
+learn_inf.predict(dest)
+