
<div align="center">
  <img width="640" alt="keremberke/yolov8m-pokemon-classification" src="https://huggingface.co/keremberke/yolov8m-pokemon-classification/resolve/main/thumbnail.jpg">
</div>

### 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](https://github.com/fcakyon/ultralyticsplus):

```bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
```

- Load model and perform prediction:

```python
from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('keremberke/yolov8m-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](https://yolov8.xyz)**
{"library_name": "ultralytics", "tags": ["ultralyticsplus", "yolov8", "ultralytics", "yolo", "vision", "image-classification", "pytorch", "awesome-yolov8-models"], "datasets": ["keremberke/pokemon-classification"], "library_version": "8.0.23", "inference": false, "model-index": [{"name": "keremberke/yolov8m-pokemon-classification", "results": [{"task": {"type": "image-classification"}, "dataset": {"name": "pokemon-classification", "type": "keremberke/pokemon-classification", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.03279, "name": "top1 accuracy"}, {"type": "accuracy", "value": 0.09699, "name": "top5 accuracy"}]}]}]}
ultralytics tensorboard v8 ultralyticsplus yolov8 yolo vision image-classification pytorch awesome-yolov8-models dataset:keremberke/pokemon-classification model-index region:us
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/pokemon-classification #model-index #region-us 


<div align="center">
  <img width="640" alt="keremberke/yolov8m-pokemon-classification" src="URL
</div>

### Supported Labels



### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
### Supported Labels ### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/pokemon-classification #model-index #region-us 
 ### Supported Labels ### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/pokemon-classification #model-index #region-us 
### Supported Labels### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models