datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
E2AM_EfficientNetV2_S
E2AM Ablation Results: EfficientNetV2-S
Energy-aware training ablation study for EfficientNetV2-S across three image-classification datasets: CIFAR-10, CIFAR-100, and Tiny-ImageNet.
Each dataset has 15 training variants (8 individual-method M0..M7, 7 cumulative ablation C0..C6) at 50 epochs, plus a 5-variant deployment pipeline (FP32 baseline, structured pruning, pruning+finetune, INT8 quantization, pruned+INT8).
Status: 45 completed variants, 0 partial.
Quick links… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/E2AM_EfficientNetV2_S.efficient-team-4a18b8
efficient-team-4a18b8
Synthetic weather test data: 57 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/SableField/efficient-team-4a18b8.Dataset-EfficientDrivingTimeDeterminationSystemimport re
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
from google.colab import drive
from sklearn.tree import export_text
from sklearn.metrics import accuracy_score
1. Mount Google Drive
drive.mount('/content/drive')
2. Baca file Excel
file_path = '/content/drive/MyDrive/Colab Notebooks/AI_GACOR_Cleaned.xlsx'
data = pd.read_excel(file_path)
3. Encode kolom 'Hari'
label_encoder_hari =… See the full description on the dataset page: https://huggingface.co/datasets/jellysquish/Dataset-EfficientDrivingTimeDeterminationSystem.