feat: reorganizar modelos em categorias e adicionar novos classificadores
Browse files- Reorganizada lista de modelos em 20 categorias por tipo de classificação
- Adicionados 22 modelos novos do Hugging Face com sufxo " new"
- Atualizada função classify para remover sufixo antes de usar modelo
---
feat: reorganize models into categories and add new classifiers
- Reorganized model list into 20 categories by classification type
- Added 22 new Hugging Face models with " new" suffix
- Updated classify function to remove suffix before using model
- app.py +85 -38
- contexto.md +3 -0
app.py
CHANGED
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@@ -7,48 +7,95 @@ MIN_ACEPTABLE_SCORE = 0.1
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MAX_N_LABELS = 5
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MODEL_2 = "nateraw/vit-age-classifier"
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MODELS = [
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"microsoft/resnet-50",
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"microsoft/resnet-
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"microsoft/resnet-
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"microsoft/
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"microsoft/
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"microsoft/
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"--
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"--
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"-
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"timm/
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"timm/mobilenetv3_large_100.ra_in1k", #Classifição geral
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"trpakov/vit-face-expression", #Classifição de expressão facial
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"rizvandwiki/gender-classification", #Classifição de gênero
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"#q-future/one-align", #Classifição geral
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"LukeJacob2023/nsfw-image-detector", #Classifição NSFW
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"vit-base-patch16-224-in21k", #Classifição geral
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"not-lain/deepfake", #Classifição deepfake
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"carbon225/vit-base-patch16-224-hentai", #Classifição hentai
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"facebook/convnext-base-224-22k-1k", #Classifição geral
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"facebook/convnext-large-224", #Classifição geral
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"facebook/convnext-tiny-224",#Classifição geral
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"nvidia/mit-b0", #Classifição geral
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"microsoft/resnet-18", #Classifição geral
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"microsoft/swinv2-base-patch4-window16-256", #Classifição geral
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"andupets/real-estate-image-classification", #Classifição de imóveis
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"timm/tf_efficientnetv2_s.in21k", #Classifição geral
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"timm/convnext_tiny.fb_in22k",
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]
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def classify(image, model):
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result= classifier(image)
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return result
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MAX_N_LABELS = 5
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MODEL_2 = "nateraw/vit-age-classifier"
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MODELS = [
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"-- General Image Classification --",
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"google/vit-base-patch16-224",
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"microsoft/resnet-50",
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"microsoft/resnet-18",
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"microsoft/resnet-34",
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"microsoft/resnet-101",
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"microsoft/resnet-152",
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"microsoft/swin-tiny-patch4-window7-224",
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"microsoft/swinv2-base-patch4-window16-256",
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"microsoft/beit-base-patch16-224-pt22k-ft22k",
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"facebook/convnext-large-224",
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"facebook/convnext-base-224-22k-1k",
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"facebook/convnext-tiny-224",
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"nvidia/mit-b0",
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"timm/resnet50.a1_in1k",
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"timm/tf_efficientnetv2_s.in21k",
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"timm/convnext_tiny.fb_in22k",
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"vit-base-patch16-224-in21k",
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"facebook/deit-base-distilled-patch16-224 new",
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"WinKawaks/vit-tiny-patch16-224 new",
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"-- Age Classification --",
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"nateraw/vit-age-classifier",
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"-- NSFW Detection --",
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"Falconsai/nsfw_image_detection",
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"LukeJacob2023/nsfw-image-detector",
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"carbon225/vit-base-patch16-224-hentai",
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"Marqo/nsfw-image-detection-384 new",
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"-- Aesthetic/Art Classification --",
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"cafeai/cafe_aesthetic",
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"shadowlilac/aesthetic-shadow",
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"pixai-labs/pixai-tagger-v0.9 new",
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"-- Face/Emotion Classification --",
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"trpakov/vit-face-expression",
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"RickyIG/emotion_face_image_classification",
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"rizvandwiki/gender-classification",
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"-- Food Classification --",
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"nateraw/food",
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"BinhQuocNguyen/food-recognition-model new",
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"-- Medical/Dermatology --",
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"google/derm-foundation new",
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"google/cxr-foundation new",
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"Anwarkh1/Skin_Cancer-Image_Classification new",
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"-- AI vs Human Detection --",
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"Ateeqq/ai-vs-human-image-detector new",
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"umm-maybe/AI-image-detector new",
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"-- Deepfake Detection --",
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"not-lain/deepfake",
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"-- Anime/Manga Classification --",
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"Readidno/anime.mili new",
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"-- Human Activity Recognition --",
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"DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition",
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"-- Clothing/Fashion --",
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"aalonso-developer/vit-base-patch16-224-in21k-clothing-classifier",
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"-- Real Estate --",
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"andupets/real-estate-image-classification",
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"-- Satellite/Remote Sensing --",
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"FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark",
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"-- Car Classification --",
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"lamnt2008/car_brands_classification new",
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"-- Document Classification --",
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"docling-project/DocumentFigureClassifier-v2.5 new",
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"-- EfficientNet (timm) --",
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"timm/efficientnet_b0.ra_in1k new",
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"timm/mobilenetv3_large_100.ra_in1k",
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"timm/mobilenetv3_small_100.lamb_in1k new",
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"-- Experimental/Future --",
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"#q-future/one-align",
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]
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def classify(image, model):
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model_name = model.replace(" new", "")
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classifier = pipeline("image-classification", model=model_name)
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result= classifier(image)
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return result
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contexto.md
ADDED
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@@ -0,0 +1,3 @@
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# Descrição
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Esta aplicação é apenas uma demostração para o hugging face comparado varios classificadores de imagens
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