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Genre Prediction

Music genre classifier trained on the Messy Mashup competition dataset.

KindOtherVersionRevision 2eedd1dCgrade

Model source

Kind
Other
File size
41.4 MB
Version
vRevision 2eedd1d
Updated
Sep 26, 2026
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0
Source
Hugging Face

Source description

Genre Classification — EfficientNet-B3

Music genre classifier trained on the Messy Mashup competition dataset.

Classes

blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock

Architecture

EfficientNet-B3 fine-tuned on mel spectrograms (128 mel bins, 5s clips, 22050 Hz).

How to load

import torch
from torchvision import models
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="Kirandwip/Genre_prediction", filename="perfection_model_99.pth")

model = models.efficientnet_b3(weights=None)
model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, 10)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

Sources

1 source
Hugging FacePrimaryVersion 2eedd1dc45b03582cb14983cfaeb98b6d42bf240
huggingface.co/Kirandwip/Genre_prediction/tree/2eedd1dc45b03582cb14983cfaeb98b6d42bf240

Verified Aug 29

Model artifacts

1 artifact

kaggle/working/best_99.pth

pth · 41.4 MB · SHA-256 7670c3dc42cc…5cf7 · Hugging Face

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Source excerpts

2 excerpts

--- language: en tags: - audio-classification - music-genre - efficientnet - pytorch license: mit --- # Genre Classification — EfficientNet-B3 Music genre classifier trained on the Messy Mashup competition dataset. ## Classes blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock ## Architecture EfficientNet-B3 fine-tuned on mel spectrograms (128 mel bins, 5s clips, 22050 Hz). ## How to load ```python import torch from torchvision import models from huggingface_hub import hf_hub_download model_path = hf_hub_download(repo_id="Kirandwip/Genre_prediction", filename="perfection_model_99.pth") model = models.efficientnet_b3(weights=None) model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, 10) model.load_state_dict(torch.load(model_path, map_location="cpu")) model.eval() ```

Source context: 0 downloads · 0 likes · Pipeline audio-classification · Repo Kirandwip/Genre_prediction