Instructions to use FishingROV/classifier_swinv2b_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use FishingROV/classifier_swinv2b_256 with timm:
import timm model = timm.create_model("hf_hub:FishingROV/classifier_swinv2b_256", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "overall_acc": 0.9662956576265872, | |
| "macro_precision": 0.7003933165522456, | |
| "macro_recall": 0.6539386550722986, | |
| "macro_f1": 0.6614371662248726, | |
| "labels": [ | |
| "dead", | |
| "king", | |
| "not_a_scallop", | |
| "queen", | |
| "recessed" | |
| ], | |
| "confusion_matrix": [ | |
| [ | |
| 52, | |
| 8, | |
| 6, | |
| 9, | |
| 6 | |
| ], | |
| [ | |
| 32, | |
| 18, | |
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| 16, | |
| 3 | |
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| [ | |
| 4, | |
| 2, | |
| 5756, | |
| 14, | |
| 5 | |
| ], | |
| [ | |
| 19, | |
| 7, | |
| 4, | |
| 266, | |
| 0 | |
| ], | |
| [ | |
| 5, | |
| 11, | |
| 37, | |
| 20, | |
| 72 | |
| ] | |
| ], | |
| "per_class": [ | |
| { | |
| "class": "dead", | |
| "precision": 0.4642857142857143, | |
| "recall": 0.6419753086419753, | |
| "f1": 0.538860103626943, | |
| "support": 81, | |
| "tp": 52 | |
| }, | |
| { | |
| "class": "king", | |
| "precision": 0.391304347826087, | |
| "recall": 0.23684210526315788, | |
| "f1": 0.2950819672131147, | |
| "support": 76, | |
| "tp": 18 | |
| }, | |
| { | |
| "class": "not_a_scallop", | |
| "precision": 0.9907056798623064, | |
| "recall": 0.9956754886697803, | |
| "f1": 0.9931843671814339, | |
| "support": 5781, | |
| "tp": 5756 | |
| }, | |
| { | |
| "class": "queen", | |
| "precision": 0.8184615384615385, | |
| "recall": 0.8986486486486487, | |
| "f1": 0.856682769726248, | |
| "support": 296, | |
| "tp": 266 | |
| }, | |
| { | |
| "class": "recessed", | |
| "precision": 0.8372093023255814, | |
| "recall": 0.496551724137931, | |
| "f1": 0.6233766233766234, | |
| "support": 145, | |
| "tp": 72 | |
| } | |
| ] | |
| } |