Image Classification
timm
LiteRT
Safetensors
coffee
plant-disease
agriculture
efficientnet
supervised-fine-tuning
Instructions to use ConnorLee08/coffee-leaf-efficientnet-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ConnorLee08/coffee-leaf-efficientnet-b2 with timm:
import timm model = timm.create_model("hf-hub:ConnorLee08/coffee-leaf-efficientnet-b2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download model-config.json from ConnorLee08/coffee-leaf-efficientnet-b2: direct link, hf CLI and curl.
- Browser
- Download file 2.94 kB
-
https://huggingface.co/ConnorLee08/coffee-leaf-efficientnet-b2/resolve/main/model-config.json
- Command line
-
hf download hf://ConnorLee08/coffee-leaf-efficientnet-b2/model-config.json
-
curl -L -o model-config.json https://huggingface.co/ConnorLee08/coffee-leaf-efficientnet-b2/resolve/main/model-config.json
2.94 kB
| { | |
| "labels": [ | |
| "cercospora", | |
| "healthy", | |
| "miner", | |
| "phoma", | |
| "rust", | |
| "red_spider_mite", | |
| "weevil_damage", | |
| "unsupported" | |
| ], | |
| "app_condition_keys": [ | |
| "cercospora", | |
| "healthy", | |
| "miner", | |
| "phoma", | |
| "rust", | |
| "mites", | |
| "weevil" | |
| ], | |
| "calibration": { | |
| "quality": { | |
| "minimum_edge_variance": 1.520114853217684, | |
| "minimum_mean_luminance": 45.59410861968994, | |
| "selection": "validation-only severe Gaussianblur challenge balancedaccuracy subject to >=90% clear acceptance" | |
| }, | |
| "validation_n": 5294, | |
| "class_thresholds": [ | |
| 0.605, | |
| 0.4, | |
| 0.4, | |
| 0.865, | |
| 0.4, | |
| 1.01, | |
| 0.4 | |
| ], | |
| "confident_thresholds": [ | |
| 0.895, | |
| 0.7050000000000001, | |
| 0.605, | |
| 0.9600000000000001, | |
| 0.4, | |
| 1.01, | |
| 0.4 | |
| ], | |
| "class_threshold_evidence": [ | |
| { | |
| "label": "cercospora", | |
| "enabled": true, | |
| "accept_threshold": 0.605, | |
| "val_true_support": 216 | |
| }, | |
| { | |
| "label": "healthy", | |
| "enabled": true, | |
| "accept_threshold": 0.4, | |
| "val_true_support": 265 | |
| }, | |
| { | |
| "label": "miner", | |
| "enabled": true, | |
| "accept_threshold": 0.4, | |
| "val_true_support": 964 | |
| }, | |
| { | |
| "label": "phoma", | |
| "enabled": true, | |
| "accept_threshold": 0.865, | |
| "val_true_support": 575 | |
| }, | |
| { | |
| "label": "rust", | |
| "enabled": true, | |
| "accept_threshold": 0.4, | |
| "val_true_support": 2101 | |
| }, | |
| { | |
| "label": "red_spider_mite", | |
| "enabled": false, | |
| "accept_threshold": 1.01, | |
| "val_true_support": 25 | |
| }, | |
| { | |
| "label": "weevil_damage", | |
| "enabled": true, | |
| "accept_threshold": 0.4, | |
| "val_true_support": 924 | |
| } | |
| ], | |
| "runtime": "LiteRT CPU int8 per-channel weight storage / float32 computation", | |
| "artifact_sha256": "ddbea87650b839dabb376f32d7e210ccc21a105024de23b3c0abf25fbbe86a01", | |
| "brightness_config_sha256": "cd2c58f04fa705204593009b940ad5aff2329742a348c83330eacb106788660e", | |
| "version": "b2-deployed-v1", | |
| "temperature": 0.7418458392824085, | |
| "selection": "Validation-only compressed runtime, current shared brightness policy; >=15 accepted and >=90%/95% precision per class" | |
| }, | |
| "input": "float32 raw RGB 0..255 NHWC", | |
| "crop_pct": 0.875, | |
| "architecture": "efficientnet_b2", | |
| "input_size": 224, | |
| "model_repository": "timm/efficientnet_b2.ra_in1k", | |
| "model_revision": "3577c4a7d84723645311bb5a9e5086f1b62ec8e2", | |
| "manifest_sha256": "e27dd7fc6483ad16064ccf4ebf1204ef1d8ad2c8881c5b6960641d3cbbc852f5", | |
| "checkpoint_sha256": "eb6a5d4daac75046befce1250bc9278d4c1d2ccfa8ccf0fe2f4302cb918e8dd8", | |
| "calibration_sha256": "8224070d8194b0df0cdfb9ea517307fab4878b7a7e13e1691605fe4be4f98f8d" | |
| } | |