Total runs:
187.8K
Run Growth:
116.1K
Growth Rate:
61.80%
Sponsored by
APIMart
keremberke / yolov8n-pokemon-classification
Model's Last Updated: February 22 2023
image-classification
Introduction of yolov8n-pokemon-classification
Model Details of yolov8n-pokemon-classification
Supported Labels
['Abra', 'Aerodactyl', 'Alakazam', 'Alolan Sandslash', 'Arbok', 'Arcanine', 'Articuno', 'Beedrill', 'Bellsprout', 'Blastoise', 'Bulbasaur', 'Butterfree', 'Caterpie', 'Chansey', 'Charizard', 'Charmander', 'Charmeleon', 'Clefable', 'Clefairy', 'Cloyster', 'Cubone', 'Dewgong', 'Diglett', 'Ditto', 'Dodrio', 'Doduo', 'Dragonair', 'Dragonite', 'Dratini', 'Drowzee', 'Dugtrio', 'Eevee', 'Ekans', 'Electabuzz', 'Electrode', 'Exeggcute', 'Exeggutor', 'Farfetchd', 'Fearow', 'Flareon', 'Gastly', 'Gengar', 'Geodude', 'Gloom', 'Golbat', 'Goldeen', 'Golduck', 'Golem', 'Graveler', 'Grimer', 'Growlithe', 'Gyarados', 'Haunter', 'Hitmonchan', 'Hitmonlee', 'Horsea', 'Hypno', 'Ivysaur', 'Jigglypuff', 'Jolteon', 'Jynx', 'Kabuto', 'Kabutops', 'Kadabra', 'Kakuna', 'Kangaskhan', 'Kingler', 'Koffing', 'Krabby', 'Lapras', 'Lickitung', 'Machamp', 'Machoke', 'Machop', 'Magikarp', 'Magmar', 'Magnemite', 'Magneton', 'Mankey', 'Marowak', 'Meowth', 'Metapod', 'Mew', 'Mewtwo', 'Moltres', 'MrMime', 'Muk', 'Nidoking', 'Nidoqueen', 'Nidorina', 'Nidorino', 'Ninetales', 'Oddish', 'Omanyte', 'Omastar', 'Onix', 'Paras', 'Parasect', 'Persian', 'Pidgeot', 'Pidgeotto', 'Pidgey', 'Pikachu', 'Pinsir', 'Poliwag', 'Poliwhirl', 'Poliwrath', 'Wigglytuff', 'Zapdos', 'Zubat']
How to use
- Install ultralyticsplus :
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
- Load model and perform prediction:
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8n-pokemon-classification')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
More models available at: awesome-yolov8-models
Runs of keremberke yolov8n-pokemon-classification on huggingface.co
703
Total runs
6
24-hour runs
4
3-day runs
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7-day runs
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30-day runs
More Information About yolov8n-pokemon-classification huggingface.co Model
Url of yolov8n-pokemon-classification
yolov8n-pokemon-classification huggingface.co Url
Provider of yolov8n-pokemon-classification huggingface.co
Other API from keremberke
Updated:May 23 2024
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