Instructions to use dima806/farm_insects_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/farm_insects_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/farm_insects_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/farm_insects_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/farm_insects_image_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- accuracy
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- f1
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---
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- accuracy
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---
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Returns farm insect type given an image.
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See https://www.kaggle.com/code/dima806/farm-insects-image-detection-vit for more details.
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```
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Classification report:
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precision recall f1-score support
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Fall Armyworms 0.7895 0.3191 0.4545 47
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Western Corn Rootworms 0.9787 0.9787 0.9787 47
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Colorado Potato Beetles 1.0000 0.9792 0.9895 48
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Thrips 0.9762 0.8723 0.9213 47
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Corn Earworms 0.9070 0.8125 0.8571 48
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Cabbage Loopers 0.9388 0.9583 0.9485 48
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Armyworms 0.6143 0.9149 0.7350 47
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Brown Marmorated Stink Bugs 1.0000 1.0000 1.0000 48
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Tomato Hornworms 0.9792 1.0000 0.9895 47
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Citrus Canker 0.9038 1.0000 0.9495 47
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Aphids 0.9020 0.9583 0.9293 48
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Corn Borers 0.8148 0.9167 0.8627 48
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Fruit Flies 1.0000 1.0000 1.0000 48
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Africanized Honey Bees (Killer Bees) 1.0000 1.0000 1.0000 48
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Spider Mites 0.9167 0.9167 0.9167 48
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accuracy 0.9090 714
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macro avg 0.9147 0.9085 0.9022 714
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weighted avg 0.9151 0.9090 0.9027 714
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```
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