[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6P5HBJHY98PSHFDPCVPVEKB":3,"skill-same:01M6P5HBJHY98PSHFDPCVPVEKB":123},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":26,"commitSha":52,"skillDirectory":53,"forksCount":13,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6P5HBJHY98PSHFDPCVPVEKB","hugging-face-vision-trainer",null,"Train object detection, image classification, and SAM or SAM2 segmentation models locally or on Hugging Face Jobs, with dataset validation and results saved to the Hub.","duc-duy-1612\u002FOnline-Delivery-Systems-Web","duc-duy-1612","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F192231384?v=4","01M6NSDKQQDKVTHZ8DZMZ1VYY6","01M6NSDKW8DDVTQHBT1HBDDJPT",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fhugging-face-vision-trainer\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"hugging face","vision","hugging face vision trainer","main","object","tree","saved","with","Image Classification","sam","dataset","critical","image","results","classification","segmentation","HuggingFace","license","official","github","2026-11-10 10:12:30",false,"---\nname: hugging-face-vision-trainer\ndescription: \"Train object detection, image classification, and SAM or SAM2 segmentation models locally or on Hugging Face Jobs, with dataset validation and results saved to the Hub.\"\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-vision-trainer\nsource_repo: huggingface\u002Fskills\nsource_type: official\ndate_added: 2026-07-01\nlicense: Apache-2.0\nlicense_source: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# Vision Model Training on Hugging Face Jobs\n\nTrain object detection, image classification, and SAM\u002FSAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub.\n\n## Detailed Guide\n\nRead [the detailed guide](references\u002Fdetailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.\n\n## When to Use This Skill\n\nUse this skill when users want to:\n- Fine-tune object detection models (D-FINE, RT-DETR v2, DETR, YOLOS) on cloud GPUs or local\n- Fine-tune image classification models (timm: MobileNetV3, MobileViT, ResNet, ViT\u002FDINOv3, or any Transformers classifier) on cloud GPUs or local\n- Fine-tune SAM or SAM2 models for segmentation \u002F image matting using bbox or point prompts\n- Train bounding-box detectors on custom datasets\n- Train image classifiers on custom datasets\n- Train segmentation models on custom mask datasets with prompts\n- Run vision training jobs on Hugging Face Jobs infrastructure\n- Ensure trained vision models are permanently saved to the Hub\n\n## Prerequisites Checklist\n\nBefore starting any training job, verify:\n\n### Account & Authentication\n- Hugging Face Account with [Pro](https:\u002F\u002Fhf.co\u002Fpro), [Team](https:\u002F\u002Fhf.co\u002Fenterprise), or [Enterprise](https:\u002F\u002Fhf.co\u002Fenterprise) plan (Jobs require paid plan)\n- Authenticated login: Check with `hf_whoami()` (tool) or `hf auth whoami` (terminal)\n- Token has **write** permissions\n- **MUST pass token in job secrets** — see directive #3 below for syntax (MCP tool vs Python API)\n\n### Dataset Requirements — Object Detection\n- Dataset must exist on Hub\n- Annotations must use the `objects` column with `bbox`, `category` (and optionally `area`) sub-fields\n- Bboxes can be in **xywh (COCO)** or **xyxy (Pascal VOC)** format — auto-detected and converted\n- Categories can be **integers or strings** — strings are auto-remapped to integer IDs\n- `image_id` column is **optional** — generated automatically if missing\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Dataset Requirements — Image Classification\n- Dataset must exist on Hub\n- Must have an **`image` column** (PIL images) and a **`label` column** (integer class IDs or strings)\n- The label column can be `ClassLabel` type (with names) or plain integers\u002Fstrings — strings are auto-remapped\n- Common column names auto-detected: `label`, `labels`, `class`, `fine_label`\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Dataset Requirements — SAM\u002FSAM2 Segmentation\n- Dataset must exist on Hub\n- Must have an **`image` column** (PIL images) and a **`mask` column** (binary ground-truth segmentation mask)\n- Must have a **prompt** — either:\n  - A **`prompt` column** with JSON containing `{\"bbox\": [x0,y0,x1,y1]}` or `{\"point\": [x,y]}`\n  - OR a dedicated **`bbox`** column with `[x0,y0,x1,y1]` values\n  - OR a dedicated **`point`** column with `[x,y]` or `[[x,y],...]` values\n- Bboxes should be in **xyxy** format (absolute pixel coordinates)\n- Example dataset: `merve\u002FMicroMat-mini` (image matting with bbox prompts)\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Critical Settings\n- **Timeout must exceed expected training time** — Default 30min is TOO SHORT. See directive #6 for recommended values.\n- **Hub push must be enabled** — `push_to_hub=True`, `hub_model_id=\"username\u002Fmodel-name\"`, token in `secrets`\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n","# Vision Model Training on Hugging Face Jobs\n\nTrain object detection, image classification, and SAM\u002FSAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub.\n\n## Detailed Guide\n\nRead [the detailed guide](references\u002Fdetailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.\n\n## When to Use This Skill\n\nUse this skill when users want to:\n- Fine-tune object detection models (D-FINE, RT-DETR v2, DETR, YOLOS) on cloud GPUs or local\n- Fine-tune image classification models (timm: MobileNetV3, MobileViT, ResNet, ViT\u002FDINOv3, or any Transformers classifier) on cloud GPUs or local\n- Fine-tune SAM or SAM2 models for segmentation \u002F image matting using bbox or point prompts\n- Train bounding-box detectors on custom datasets\n- Train image classifiers on custom datasets\n- Train segmentation models on custom mask datasets with prompts\n- Run vision training jobs on Hugging Face Jobs infrastructure\n- Ensure trained vision models are permanently saved to the Hub\n\n## Prerequisites Checklist\n\nBefore starting any training job, verify:\n\n### Account & Authentication\n- Hugging Face Account with [Pro](https:\u002F\u002Fhf.co\u002Fpro), [Team](https:\u002F\u002Fhf.co\u002Fenterprise), or [Enterprise](https:\u002F\u002Fhf.co\u002Fenterprise) plan (Jobs require paid plan)\n- Authenticated login: Check with `hf_whoami()` (tool) or `hf auth whoami` (terminal)\n- Token has **write** permissions\n- **MUST pass token in job secrets** — see directive #3 below for syntax (MCP tool vs Python API)\n\n### Dataset Requirements — Object Detection\n- Dataset must exist on Hub\n- Annotations must use the `objects` column with `bbox`, `category` (and optionally `area`) sub-fields\n- Bboxes can be in **xywh (COCO)** or **xyxy (Pascal VOC)** format — auto-detected and converted\n- Categories can be **integers or strings** — strings are auto-remapped to integer IDs\n- `image_id` column is **optional** — generated automatically if missing\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Dataset Requirements — Image Classification\n- Dataset must exist on Hub\n- Must have an **`image` column** (PIL images) and a **`label` column** (integer class IDs or strings)\n- The label column can be `ClassLabel` type (with names) or plain integers\u002Fstrings — strings are auto-remapped\n- Common column names auto-detected: `label`, `labels`, `class`, `fine_label`\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Dataset Requirements — SAM\u002FSAM2 Segmentation\n- Dataset must exist on Hub\n- Must have an **`image` column** (PIL images) and a **`mask` column** (binary ground-truth segmentation mask)\n- Must have a **prompt** — either:\n  - A **`prompt` column** with JSON containing `{\"bbox\": [x0,y0,x1,y1]}` or `{\"point\": [x,y]}`\n  - OR a dedicated **`bbox`** column with `[x0,y0,x1,y1]` values\n  - OR a dedicated **`point`** column with `[x,y]` or `[[x,y],...]` values\n- Bboxes should be in **xyxy** format (absolute pixel coordinates)\n- Example dataset: `merve\u002FMicroMat-mini` (image matting with bbox prompts)\n- **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)\n\n### Critical Settings\n- **Timeout must exceed expected training time** — Default 30min is TOO SHORT. See directive #6 for recommended values.\n- **Hub push must be enabled** — `push_to_hub=True`, `hub_model_id=\"username\u002Fmodel-name\"`, token in `secrets`\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n",{"name":5,"risk":34,"source":48,"license":14,"date_added":49,"description":7,"source_repo":50,"source_type":41,"license_source":51},"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-vision-trainer",1782864000000,"huggingface\u002Fskills","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE","f3312ba749561b6159f477dbfc9313c875d40a56",".agents\u002Fskills_archive\u002Fhugging-face-vision-trainer","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612","2026-10-25 23:50:58",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill hugging-face-vision-trainer",{"tool":63,"command":64},"bunx","bunx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill 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Configure identity-based access, micro-segmentation, and continuous verification. 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