Spaces:
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noelsinghsr
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bd722fa
initial commit
Browse files- .gitattributes +1 -0
- airplane_uncompiled.pth +3 -0
- airplanes.zip +3 -0
- app.py +51 -0
- examples/f16.jpg +0 -0
- examples/warthog.jpg +0 -0
- model.py +11 -0
- requirements.txt +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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airplane_uncompiled.pth filter=lfs diff=lfs merge=lfs -text
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airplane_uncompiled.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:28c3c11a6aba10b22eac86bccabcd23e56bb2a3258f380ba9f6c258b173e4ddb
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size 31356145
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airplanes.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:b14163fcf18a027d04c245c56f525076a26c37799c95a7688c1f4cf2d3f99f19
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size 30536510
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app.py
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import gradio as gr
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import os
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import torch
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from model import load_model
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from timeit import default_timer as timer
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from typing import Tuple, Dict
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# class names
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class_names = ['A-10', 'C-130', 'F-16']
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model, transform = load_model()
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# predict function
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def predict(img):
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start_time = timer()
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img = transform(img).unsqueeze(0)
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model.eval()
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with torch.inference_mode():
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pred_probs = torch.softmax(model(img), dim=1)
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pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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end_time = timer()
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pred_time = round(end_time - start_time, 4)
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return pred_labels_and_probs, pred_time
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title = "Military Aircraft predictor - Efficinet_B2 Computer Vision Model (PyTorch)"
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description = "An EfficientNetB2 feature extractor computer vision model to classify Custom Dataset of F-16 Fighter Jet, C-130 Hercules, A-10 Warthog"
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article = "Created in SageMaker Studio"
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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# Gradio app
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demo = gr.Interface(fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=[gr.Label(num_top_classes=10, label="Predictions"),
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gr.Number(label="Prediction time (s)")],
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examples=example_list,
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title=title,
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description=description,
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article=article)
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examples/f16.jpg
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examples/warthog.jpg
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model.py
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import torch
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import torchvision
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from torch import nn
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def load_model():
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loaded_model = torch.load('demo/airplanes/airplane_uncompiled.pth', map_location=torch.device('cpu'))
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model_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
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transforms = model_weights.transforms()
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return loaded_model, transforms
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requirements.txt
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torch
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torchvision
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gradio
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