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 evaluation/single-image-timing.json from ConnorLee08/coffee-leaf-efficientnet-b2: direct link, hf CLI and curl.
- Browser
- Download file 178 kB
-
https://huggingface.co/ConnorLee08/coffee-leaf-efficientnet-b2/resolve/main/evaluation/single-image-timing.json
- Command line
-
hf download hf://ConnorLee08/coffee-leaf-efficientnet-b2/evaluation/single-image-timing.json
-
curl -L -o single-image-timing.json https://huggingface.co/ConnorLee08/coffee-leaf-efficientnet-b2/resolve/main/evaluation/single-image-timing.json
178 kB
| { | |
| "generated_at_utc": "2026-10-04T02:16:57.254745+00:00", | |
| "method": { | |
| "description": "Fresh single-image desktop response timing with every model already loaded", | |
| "unique_images": 8, | |
| "image_selection": "First example per class from the main comparator's seeded 64-image benchmark selection; no prediction-based selection", | |
| "rounds": 3, | |
| "repetitions_per_image": 12, | |
| "timed_predictions_per_model": 96, | |
| "warmup_responses_per_model": 8, | |
| "invocation_orders": [ | |
| [ | |
| "b0", | |
| "b1", | |
| "b3", | |
| "b2" | |
| ], | |
| [ | |
| "b1", | |
| "b2", | |
| "b0", | |
| "b3" | |
| ], | |
| [ | |
| "b2", | |
| "b3", | |
| "b1", | |
| "b0" | |
| ], | |
| [ | |
| "b3", | |
| "b0", | |
| "b2", | |
| "b1" | |
| ] | |
| ], | |
| "balance": "Each image uses all four Williams Latin-square orders in every round; model positions and directed adjacent pairs are balanced", | |
| "total_includes": [ | |
| "Fresh local image open/decode and canonical resize/crop to float32 input", | |
| "Shared current quality helper, including reopening the image for alpha-aware exposure", | |
| "Input tensor transfer, model invocation, and output tensor retrieval", | |
| "Exact shared per-class confidence, brightness, and blur decision gate" | |
| ], | |
| "inference_only": "interpreter.invoke() wall time inside the same response trial", | |
| "excludes": [ | |
| "One-time model loading, tensor allocation, imports, and warmup", | |
| "Artifact/image hashing, metadata validation, result checks, and report serialization", | |
| "Camera capture, user selection, network, mobile bridge, and UI rendering" | |
| ], | |
| "filesystem_cache": "Images reopen every trial; the operating-system file cache is warm and is not flushed", | |
| "preprocessing_note": "Measures the existing desktop evaluation helpers, including their configuration read and repeated image decoding; the mobile implementation can differ", | |
| "inference_policy": "Every image invokes the model before the decision gate, including images that fail quality checks", | |
| "num_threads": 4, | |
| "torch_interop_threads": 1, | |
| "input_shape": [ | |
| 1, | |
| 224, | |
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| "input_dtype": "float32", | |
| "clock": "time.perf_counter", | |
| "host": "connor", | |
| "cpu": "AMD64 Family 25 Model 97 Stepping 2, AuthenticAMD", | |
| "logical_cpus": 16, | |
| "physical_phone_benchmarked": false, | |
| "qualification": "Desktop response measurements do not establish physical phone latency; repeated trials are not independent images" | |
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