Loading the evaluated parameters
Use the model-specific configuration and source revision listed in MANIFEST.json.
Keep the dataset's original category order, image pipeline, angle convention,
matching protocol and score threshold; changing them changes the experiment.
The files are not a Transformers from_pretrained package or a hosted inference
service.
Download one fixed repository revision
The following example obtains the current public revision once and uses it for both files. For a cited experiment, retain that revision and the manifest hashes. The code downloads parameters but does not run inference.
from huggingface_hub import HfApi, hf_hub_download
import torch
repo = "PeterPonyu/rotcert-models"
revision = HfApi(token=False).model_info(repo).sha
folder = "dior/train-only-validation-check/oriented-rcnn/seed-0"
weights = hf_hub_download(repo, folder + "/inference.pth", revision=revision, token=False)
config = hf_hub_download(repo, folder + "/config.py", revision=revision, token=False)
checkpoint = torch.load(weights, map_location="cpu", weights_only=True)
state_dict = checkpoint["state_dict"]
All public weights contain only tensors. Use a PyTorch version supporting
weights_only=True. The export was checked in the original framework runtime and
again in a separate CPU runtime; this is not a compatibility test of every
PyTorch or CUDA version. Do not disable restricted loading for an unrelated
downloaded file.
MMRotate detector families
The evaluated source revision is
open-mmlab/mmrotate@3ff004eb21ea040455b5585db229edba4037f1bf.
The original evaluation environment used MMRotate 1.0.0rc1, MMDetection 3.3.0,
MMEngine 0.10.5, MMCV 2.1.0 and PyTorch 2.1.0 with CUDA 12.1. These are recorded
versions, not a promise that installation works on every current platform.
Each resolved config.py includes the actual model, image
normalization, test transforms, box coding, angle representation and evaluation
settings. The exact category order from the original checkpoints is in
MODEL-METADATA.json; do not infer it from a different dataset implementation.
Replace only the dataset and output locations for an authorized local
copy. The public derivatives use the top-level state_dict accepted by the
standard MMEngine checkpoint loader. Evaluation uses an explicit checkpoint,
not resume mode; do not substitute ema_state_dict from a training checkpoint.
Within the identified MMRotate source and compatible environment, use
tools/test.py with the matching config.py and inference.pth. Read the
configuration before setting local data locations: a command-line data_root
override alone does not rewrite all already-resolved nested paths. Standard
detector evaluation does not reproduce the later RotCert calibration analyses
without their separate source-level records. The saved configuration's default
test-loader list is ImageSets/Main/trainval.txt; that is not itself the official
held-out validation role used by the later export driver. Evaluation on the
reported validation/test populations also requires the correct official lists,
role IDs and documented driver overrides. Do not interpret a default test-loader
run as reproduction of the validation-check results.
EAV-DETR on CODrone
Obtain the identified upstream source separately:
zzzhak/EAV-DETR@fb99da7b0f49edffcd1fa9e45a4424e9ae86e1eb.
It is not redistributed here. The public file is a top-level model mapping made
from the final evaluated model branch. The preserved original and export
receipts identify that branch; no EMA branch is present or substituted.
The model construction and strict parameter load can be checked as follows in
that source and its compatible environment. config_path and weights_path
below refer to the downloaded model's own files, not upstream example weights.
This snippet constructs and loads the model; it does not rerun the full dataset.
from huggingface_hub import HfApi, hf_hub_download
import torch
from src.core import YAMLConfig
repo = "PeterPonyu/rotcert-models"
revision = HfApi(token=False).model_info(repo).sha
folder = "codrone/final-epoch-evaluation/eav-detr/seed-42"
config_path = hf_hub_download(repo, folder + "/config.yml", revision=revision, token=False)
weights_path = hf_hub_download(repo, folder + "/inference.pth", revision=revision, token=False)
cfg = YAMLConfig(config_path)
model = cfg.model.cpu()
state = torch.load(weights_path, map_location="cpu", weights_only=True)["model"]
model.load_state_dict(state, strict=True)
model.eval()
The original export used PyTorch 2.4.0 with CUDA 12.1. Use the category order
in MODEL-METADATA.json (checked against all ten export receipts), the supplied
configuration and the pinned CODrone loader. Model construction may obtain the
configured pretrained backbone before loading the final model; this dependency
is not bundled or newly licensed here. The reported full exports used all
56,000 validation tiles and 84,056 test tiles, retaining queries with maximum
class probability at least 0.05. A monitoring loader may limit samples; running
it unchanged is not the full exported experiment. Box outputs are normalized
center-x, center-y, width, height and angle in radians. AP from those retained
outputs remains score-truncated. Native EAV and common-HCP analysis also require
their distinct matching and calibration protocols.
The reproduction driver for the complete exports and statistical analyses is a separate software/input requirement. The illustrative loading snippets are not an assertion that full inference was newly rerun for this release.