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swinv2-tiny-patch4-window16-256-mic

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swinv2-tiny-patch4-window16-256-mic
File size: 2,079 Bytes
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{
  "mic": "0.3",
  "name": "Swinv2 image classification",
  "usage": "AutoImageProcessor + AutoModelForImageClassification, batched synthetic images, inference and one training step",
  "architecture": "Swinv2ForImageClassification",
  "checkpoints": {
    "microsoft/swinv2-tiny-patch4-window16-256": {
      "fixture": "huggingface-mic/swinv2-tiny-patch4-window16-256-mic"
    }
  },
  "fixtures": {
    "huggingface-mic/swinv2-tiny-patch4-window16-256-mic": {
      "built_from": "microsoft/swinv2-tiny-patch4-window16-256",
      "covers": [
        "microsoft/swinv2-tiny-patch4-window16-256"
      ],
      "build": {
        "seed": 17,
        "accept_insensitive": {
          "pixel_values:halve": "The patch embedding is layer-normalized and its convolution bias initializes to zero, so a uniformly scaled image yields the same random-weight features"
        }
      }
    }
  },
  "integrations": {
    "transformers": {
      "entry_point": "integrations/transformers/usage.md",
      "format": "runnable-markdown",
      "requires": [
        "transformers>=5",
        "torch"
      ],
      "targets": [
        "fixtures",
        "checkpoints"
      ],
      "variants": {
        "default": {},
        "eager": {
          "attn_implementation": "eager"
        },
        "bfloat16": {
          "dtype": "bfloat16"
        }
      },
      "training": true,
      "resources": {
        "fixture": {
          "memory_gb": 2,
          "download_mb": 30
        },
        "microsoft/swinv2-tiny-patch4-window16-256": {
          "memory_gb": 4,
          "download_mb": 120
        }
      }
    }
  },
  "outputs": {
    "runtime": {
      "compare": "exact"
    },
    "predicted_classes": {
      "compare": "exact"
    },
    "logits.probe": {
      "compare": "allclose",
      "atol": 0.0001,
      "rtol": 0.0001
    },
    "training.loss": {
      "compare": "allclose",
      "atol": 0.0001,
      "rtol": 0.0001
    },
    "training.gradients": {
      "compare": "all_true"
    },
    "training.updated": {
      "compare": "true"
    }
  }
}