Instructions to use KRMayD/COD10K_CAM_5Way_Segmentation_Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use KRMayD/COD10K_CAM_5Way_Segmentation_Checkpoints with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:KRMayD/COD10K_CAM_5Way_Segmentation_Checkpoints') tokenizer = open_clip.get_tokenizer('hf-hub:KRMayD/COD10K_CAM_5Way_Segmentation_Checkpoints') - Notebooks
- Google Colab
- Kaggle
| { | |
| "train_csv": "/home/msko021220/dataset/COD10K_GMPO_Used_Data/captions/cod10k_train_cam_dpo_1caption_sd_bgpos_vs_bgexplicitneg_exclude_mmcamobj_easyhard_2886.csv", | |
| "train_csv_sha256": "7f2dde8a9062a9a8c7080c8c6d8c6e3c4981ec7056e04615761e10d669a95630", | |
| "train_pairs": 2886, | |
| "excluded_mmcamobj_easyhard_images": 154, | |
| "positive_input": [ | |
| "filename", | |
| "Caption" | |
| ], | |
| "gmpo_only_inputs": [ | |
| "filename_neg", | |
| "Caption_neg" | |
| ], | |
| "initial_and_reference_checkpoint": "/home/msko021220/dataset/clip_reference_checkpoints/openai_clip_vit_b_32_quickgelu_openclip_state_dict.pt", | |
| "initial_checkpoint_sha256": "851b42d7c83e629a8c1d4b2e00cf94e7d9e9b414feeee34592228f495b08afef", | |
| "encoder": "ViT-B-32-quickgelu", | |
| "shared_train_budget": { | |
| "epochs": 3, | |
| "seed": 0 | |
| }, | |
| "gmpo": { | |
| "optimizer": { | |
| "lr": 1e-06, | |
| "weight_decay": 0.01, | |
| "warmup_steps": 10, | |
| "beta_dpo": 10.0 | |
| }, | |
| "ddp_gpus": [ | |
| 0, | |
| 1 | |
| ], | |
| "batch_per_gpu": 128, | |
| "global_batch": 256, | |
| "variants": [ | |
| "global_ind", | |
| "sample_ind" | |
| ] | |
| }, | |
| "clip_and_cliprefine_native_optimizer": { | |
| "batch_size": 32, | |
| "lr": 2e-05, | |
| "weight_decay": 0.05, | |
| "warmup_epochs": 1, | |
| "gpus": { | |
| "clip": 0, | |
| "cliprefine": 1 | |
| } | |
| }, | |
| "evaluation": { | |
| "test_split": "COD10K CAM Test", | |
| "samples": 2026, | |
| "prompt": "There is a/an {animal} visually blended into its surroundings.", | |
| "vbeta": 1.0, | |
| "vvar": 0.3, | |
| "vlayer": 8, | |
| "seed": 42, | |
| "nsd_tolerance_px": 2.0, | |
| "sam_or_postprocessing": "unchanged cod10k_saliency_hparam_search pipeline" | |
| } | |
| } | |