Atharv Subhekar
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Application Commit
Browse files- .DS_Store +0 -0
- sample_images/train_142.jpg +0 -0
- sample_images/train_32.jpg +0 -0
- sample_images/train_59.jpg +0 -0
- sample_images/train_67.jpg +0 -0
- sample_images/train_75.jpg +0 -0
- sample_images/train_92.jpg +0 -0
- satellite_app.py +75 -0
.DS_Store
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Binary file (6.15 kB). View file
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sample_images/train_142.jpg
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sample_images/train_32.jpg
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sample_images/train_59.jpg
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sample_images/train_67.jpg
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sample_images/train_75.jpg
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sample_images/train_92.jpg
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satellite_app.py
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# -*- coding: utf-8 -*-
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"""satellite_app.ipynb
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1HCITtw0z2BJO0z9GmsspLM1NzrLdft27
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"""
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!pip install gradio --quiet
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!pip install -Uq transformers datasets timm accelerate evaluate
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import gradio as gr
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from safetensors.torch import load_model
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from timm import create_model
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from huggingface_hub import hf_hub_download
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from datasets import load_dataset
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import torch
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import torchvision.transforms as T
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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safe_tensors = hf_hub_download(repo_id="subhuatharva/swim-224-base-satellite-image-classification", filename="model.safetensors")
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model_name = 'swin_s3_base_224'
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# intialize the model
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model = create_model(
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model_name,
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num_classes=17
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)
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load_model(model,safe_tensors)
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def one_hot_decoding(labels):
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class_names = ['conventional_mine', 'habitation', 'primary', 'water', 'agriculture', 'bare_ground', 'cultivation', 'blow_down', 'road', 'cloudy', 'blooming', 'partly_cloudy', 'selective_logging', 'artisinal_mine', 'slash_burn', 'clear', 'haze']
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id2label = {idx:c for idx,c in enumerate(class_names)}
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id_list = []
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for idx,i in enumerate(labels):
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if i == 1:
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id_list.append(idx)
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true_labels = []
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for i in id_list:
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true_labels.append(id2label[i])
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return true_labels
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def model_output(image):
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image = cv2.imread(name)
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PIL_image = Image.fromarray(image.astype('uint8'), 'RGB')
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img_size = (224,224)
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test_tfms = T.Compose([
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T.Resize(img_size),
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T.ToTensor(),
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])
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img = test_tfms(PIL_image)
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with torch.no_grad():
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logits = model(img.unsqueeze(0))
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predictions = logits.sigmoid() > 0.5
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predictions = predictions.float().numpy().flatten()
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pred_labels = one_hot_decoding(predictions)
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output_text = " ".join(pred_labels)
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return output_text
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app = gr.Interface(fn=model_output, inputs="image", outputs="text")
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app.launch()
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