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blasisd/
musheff

Name: musheff
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Musheff: Mushroom Classification Model

An EfficientNet-b3 model fine-tuned for classifying mushroom images into the 12 popular Russian mushroom classes corresponding to species and edibility. Fine-tuned on the 12_popular_russia_mushrooms_edible_poisonous dataset.

Model Details

Architecture

Base Model: EfficientNet-b3 (pretrained on ImageNet)
Fine-tuning Approach:
- Frozen base layers
- Modified classification head
  - dropout_rate: 0.3
  - num_classes: 12
Loss Function: CrossEntropyLoss
Optimizer: Adam (lr=0.001)

Performance

Dataset	Accuracy
Validation	96.88%
Test	95.68%

Training Strategy

Custom Split Ratio:
- 80% Training
- 10% Validation
- 10% Test
Training Duration:
- Maximum 15 epochs with early stopping
- Best model checkpoint tracking
Overfitting Prevention:
- Custom data splits for improved variation
- Early stopping mechanism (patience=3 epochs, min_delta=0.001)
- Validation performance monitoring

How to use

Here is how to use Musheff model to classify a mushroom image into one of the 12 Russian classes (species and edibility):

Option 1: Using the HuggingFace Transformers Model

python

import random

import torch

from datasets import load_dataset
from transformers import (
    AutoImageProcessor,
    AutoModel,
)

test_dataset = load_dataset(
    "SoFa325/12_popular_russia_mushrooms_edible_poisonous", split="test"
)

test_len = len(test_dataset)

# Pick a random image from test set
random_index = random.randint(0, test_len)

image = test_dataset["image"][random_index]

preprocessor = AutoImageProcessor.from_pretrained(
    "blasisd/musheff",
    trust_remote_code=True,
    use_fast=True,
)


model = AutoModel.from_pretrained(
    "blasisd/musheff",
    trust_remote_code=True,
    low_cpu_mem_usage=True,  # Activates memory-efficient loading
    device_map="auto",  # Distributes layers across devices
)

inputs = preprocessor(image, return_tensors="pt").to(model.device)

model.eval()
with torch.inference_mode():
    logits = model(inputs["pixel_values"])

# model predicts one of the 12 potential mushroom classes
predicted_label = logits.argmax(dim=1).item()

print(f"True label: {test_dataset['label'][random_index]}")
print(f"Predicted label: {model.config.id2label[predicted_label]}"),

NOTE: You may optionally replace AutoModel with AutoModelForImageClassification in the code above. The Musheff model has been registered for both classes, which can be used with the same parameters.

Option 2: Using the PyTorch Model

Alternatively, you can download the files from the repository locally and follow the steps below:

python

import json
import random

import torch

from datasets import load_dataset
from torchvision import models

from model import Musheff


# Device agnostic
device = "cuda" if torch.cuda.is_available() else "cpu"

test_dataset = load_dataset(
    "SoFa325/12_popular_russia_mushrooms_edible_poisonous", split="test"
)

test_len = len(test_dataset)

# Pick a random image from test set
random_index = random.randint(0, test_len)

image = test_dataset["image"][random_index]

with open("config.json", "r") as json_fp:
    config = json.load(json_fp)

model = Musheff(config)
model.model.load_state_dict(torch.load("musheff.pth"))
model.to(device=device)

transform = models.EfficientNet_B3_Weights.DEFAULT.transforms()

img = transform(image)

# Expecting 4D shape i.e. (batch_size, channels, height, width)
img = img.unsqueeze(0)

model.eval()

with torch.inference_mode():
    logits = model(img.to(device))

# model predicts one of the 12 potential mushroom classes
predicted_label = logits.argmax(dim=1).item()

with open("config.json", "r") as json_fp:
    id2label = json.load(json_fp).get("id2label")

print(f"True label: {test_dataset['label'][random_index]}")
print(f"Predicted label: {id2label[str(predicted_label)]}")

Dependencies

Install required Python packages using either method.

Option 1: Direct installation

Run this command in your terminal (recommended inside a virtual environment):

pip install accelerate==1.8.1 datasets==3.6.0 pillow==11.1.0 torch==2.6.0 torchvision==0.21.0 transformers==4.53.1

Option 2: Requirements file

Download requirements.txt from the repository
Run in terminal (recommended inside a virtual environment):

pip install -r requirements.txt

Intended Uses & Limitations

Recommended Use Cases

Educational mushroom identification apps
Preliminary screening of common Russian mushrooms
Integration with foraging guide applications

Limitations

Geographic Specificity: Only recognizes 12 mushroom species common in Russia
Safety Critical: NOT SUITABLE for consumption decisions - always consult human experts