datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SnakeAI_TF_PPO_V0Action mask has been implemented, the model has been updated to support 'Training Resumption' after system disruption. Utilizing the same training parameters as the "Full Reinforcement learning Agent", this agent prioritizes survival over rewards. It's playtime for 100 games is 6hrs, compared to 2hrs for the FRLA.
This demonstrates the agent is adapting for survival, but not to the desired goal of higher scores/reward. #10000000 training timesteps.
Training Hyperparameters is the same as the… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/SnakeAI_TF_PPO_V0.pp-ocr-mnn-eval
PP-OCR MNN evaluation dataset (811-cell matrix)
Images (273) + canonical paddle.inference baselines (808 json) + configs
for scoring pp-ocr-mnn outputs. See README.md and
https://github.com/baicai1145/pp-ocr-mnn (tools/score.py).
A single-file snapshot is also included as ppocr-eval-dataset.tar.zst.
SnakeAI_TF_PPO_V1The Hebrew word נָחָשׁ (Nāḥāš) is used in the Hebrew Bible to identify the serpent that appears in Genesis 3:1, in the Garden of Eden.
This contains #7000000 training parameters/timestep for Snake_AI game using TensorFlow 2.XX.
Best score and performance comes from data #4800000 dataset for ActorCritic, with an average score of 72 with no action mask/upfront rules. Full reinforcement learning with score/reward as a priority
Agent score can be improved with the combination of more training and… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/SnakeAI_TF_PPO_V1.robust-humanoid-ppo-results
RobustHumanoid PPO Results
This dataset contains RobustHumanoid benchmark results for a Stable-Baselines3 PPO
policy evaluated on Humanoid-v4 under out-of-distribution MuJoCo physics.
The benchmark evaluates a fixed policy over a 20 x 20 grid of ground friction and
actuator torque scale values, with 5 episodes per grid point.
Benchmark Configuration
Benchmark: RobustHumanoid
Environment: Humanoid-v4
Policy name: ppo_humanoid
Policy type: Stable-Baselines3 PPO… See the full description on the dataset page: https://huggingface.co/datasets/kaus4004/robust-humanoid-ppo-results.permis-ocr-ppocrv6
OCR with PP-OCRv6 Medium
Plain-text OCR results for images from mehdigououiad/permis-ocr-bench, produced by
PaddlePaddle's PP-OCRv6
medium pipeline (34.5M (22M det + 19M rec)).
Processing details
Source: mehdigououiad/permis-ocr-bench
Model: PP-OCRv6_medium (PP-OCRv6_medium_det + PP-OCRv6_medium_rec)
Tier: medium (34.5M (22M det + 19M rec))
Recognition accuracy: 83.2%
Languages: 50 languages (zh, zh-Hant, en, ja + 46 Latin-script)
Engine: paddle_static
Samples: 2… See the full description on the dataset page: https://huggingface.co/datasets/mehdigououiad/permis-ocr-ppocrv6.mllm_ppo_datasetpp-ocrv6-smoke-v4
OCR with PP-OCRv6 TINY
Plain-text OCR results for images from davanstrien/ufo-ColPali, produced by
PaddlePaddle's PP-OCRv6
tiny pipeline (1.5M (0.4M det + 1.1M rec)).
Processing details
Source: davanstrien/ufo-ColPali
Model: PP-OCRv6_tiny (PP-OCRv6_tiny_det + PP-OCRv6_tiny_rec)
Tier: tiny (1.5M (0.4M det + 1.1M rec))
Recognition accuracy: 73.5%
Languages: 49 languages (en, zh only — no ja)
Engine: paddle_static
Samples: 3
Processing time: 0.41 min
Processing… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/pp-ocrv6-smoke-v4.twitter-saozilove-2025.02.02-1885918320769261648-VRkARCkI_PpOAfKC-part1PPOO1only_ppo_ver_samPPOCR-weightsppocrlabelflux_one_word_ppocr_filteredBuildings-Change