Question about pretrained weight path

[longdaothanh]

I can see in the config file we need to set the path of ( pre-trained weight ), Not sure where to get it from.

Model -> sent1floods11_config.py
Need to Set path to pretrained backbone weights
pretrained_weights_path = ""

Best

[franrubi]

You can find the pre-trained weights file at this location: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-sen1floods11/tree/main, is that what you are looking for?

[longdaothanh]

You can find the pre-trained weights file at this location: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-sen1floods11/tree/main, is that what you are looking for?

Hi, do you mean this file: (https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-sen1floods11/blob/main/sen1floods11_Prithvi_100M.pth) ?

One other question: can i also use this weight: (https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/Prithvi_100M.pt) for fine-tuning?

Thank you so much

[franrubi]

yes to the first question, ".../sen1floods11_Prithvi_100M.pth" file keeps the weights learned for water-detection/flood downstream task.
And ".../Prithvi_100M.pth" contains the weights of the foundation model, on top of that you can finetune your own downstream task.

[longdaothanh]

yes to the first question, ".../sen1floods11_Prithvi_100M.pth" file keeps the weights learned for water-detection/flood downstream task. And ".../Prithvi_100M.pth" contains the weights of the foundation model, on top of that you can finetune your own downstream task.

Let's say i want to finetune for my own task. Do I use this weight ".../Prithvi_100M.pth" and use my own dataset instead and follow the finetuning instruction from [README.md] ? I am new to this.

Thank you so much

[franrubi]

From paper https://arxiv.org/abs/2310.18660 we can read the following:
“We thoroughly examine and assess the effect of Prithvi’s pre-trained weights on downstream tasks. We compare learning curves between 1) fine-tuning the entire model, 2) fine-tuning solely the decoder for the downstream task, and 3) training the model without utilizing Prithvi’s pre-trained weights. Our experiments show that the pre-trained model accelerates the fine-tuning process compared to leveraging randomly initialized weights."
So option 2 is the best approach.
In MMSegmentation framework setup file, “pretrained_weights_path” sentence points to the path where Prithvi_100M.pth has to be placed.

[longdaothanh]

From paper https://arxiv.org/abs/2310.18660 we can read the following: “We thoroughly examine and assess the effect of Prithvi’s pre-trained weights on downstream tasks. We compare learning curves between 1) fine-tuning the entire model, 2) fine-tuning solely the decoder for the downstream task, and 3) training the model without utilizing Prithvi’s pre-trained weights. Our experiments show that the pre-trained model accelerates the fine-tuning process compared to leveraging randomly initialized weights." So option 2 is the best approach. In MMSegmentation framework setup file, “pretrained_weights_path” sentence points to the path where Prithvi_100M.pth has to be placed.

2024-06-20 19:17:37,262 - mmseg - INFO - Set random seed to 2098588967, deterministic: False
load from C:/Masterarbeit/burn_scars_Prithvi_100M.pth
load checkpoint from local path: C:/Masterarbeit/burn_scars_Prithvi_100M.pth
The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.cls_token, backbone.pos_embed, backbone.patch_embed.proj.weight, backbone.patch_embed.proj.bias, backbone.blocks.0.norm1.weight, backbone.blocks.0.norm1.bias, backbone.blocks.0.attn.qkv.weight, backbone.blocks.0.attn.qkv.bias, backbone.blocks.0.attn.proj.weight, backbone.blocks.0.attn.proj.bias, backbone.blocks.0.norm2.weight, backbone.blocks.0.norm2.bias, backbone.blocks.0.mlp.fc1.weight, backbone.blocks.0.mlp.fc1.bias, backbone.blocks.0.mlp.fc2.weight, backbone.blocks.0.mlp.fc2.bias, backbone.blocks.1.norm1.weight, backbone.blocks.1.norm1.bias, backbone.blocks.1.attn.qkv.weight, backbone.blocks.1.attn.qkv.bias, backbone.blocks.1.attn.proj.weight, backbone.blocks.1.attn.proj.bias, backbone.blocks.1.norm2.weight, backbone.blocks.1.norm2.bias, backbone.blocks.1.mlp.fc1.weight, backbone.blocks.1.mlp.fc1.bias, backbone.blocks.1.mlp.fc2.weight, backbone.blocks.1.mlp.fc2.bias, backbone.blocks.2.norm1.weight, backbone.blocks.2.norm1.bias, backbone.blocks.2.attn.qkv.weight, backbone.blocks.2.attn.qkv.bias, backbone.blocks.2.attn.proj.weight, backbone.blocks.2.attn.proj.bias, backbone.blocks.2.norm2.weight, backbone.blocks.2.norm2.bias, backbone.blocks.2.mlp.fc1.weight, backbone.blocks.2.mlp.fc1.bias, backbone.blocks.2.mlp.fc2.weight, backbone.blocks.2.mlp.fc2.bias, backbone.blocks.3.norm1.weight, backbone.blocks.3.norm1.bias, backbone.blocks.3.attn.qkv.weight, backbone.blocks.3.attn.qkv.bias, backbone.blocks.3.attn.proj.weight, backbone.blocks.3.attn.proj.bias, backbone.blocks.3.norm2.weight, backbone.blocks.3.norm2.bias, backbone.blocks.3.mlp.fc1.weight, backbone.blocks.3.mlp.fc1.bias, backbone.blocks.3.mlp.fc2.weight, backbone.blocks.3.mlp.fc2.bias, backbone.blocks.4.norm1.weight, backbone.blocks.4.norm1.bias, backbone.blocks.4.attn.qkv.weight, backbone.blocks.4.attn.qkv.bias, backbone.blocks.4.attn.proj.weight, backbone.blocks.4.attn.proj.bias, backbone.blocks.4.norm2.weight, backbone.blocks.4.norm2.bias, backbone.blocks.4.mlp.fc1.weight, backbone.blocks.4.mlp.fc1.bias, backbone.blocks.4.mlp.fc2.weight, backbone.blocks.4.mlp.fc2.bias, backbone.blocks.5.norm1.weight, backbone.blocks.5.norm1.bias, backbone.blocks.5.attn.qkv.weight, backbone.blocks.5.attn.qkv.bias, backbone.blocks.5.attn.proj.weight, backbone.blocks.5.attn.proj.bias, backbone.blocks.5.norm2.weight, backbone.blocks.5.norm2.bias, backbone.blocks.5.mlp.fc1.weight, backbone.blocks.5.mlp.fc1.bias, backbone.blocks.5.mlp.fc2.weight, backbone.blocks.5.mlp.fc2.bias, backbone.blocks.6.norm1.weight, backbone.blocks.6.norm1.bias, backbone.blocks.6.attn.qkv.weight, backbone.blocks.6.attn.qkv.bias, backbone.blocks.6.attn.proj.weight, backbone.blocks.6.attn.proj.bias, backbone.blocks.6.norm2.weight, backbone.blocks.6.norm2.bias, backbone.blocks.6.mlp.fc1.weight, backbone.blocks.6.mlp.fc1.bias, backbone.blocks.6.mlp.fc2.weight, backbone.blocks.6.mlp.fc2.bias, backbone.blocks.7.norm1.weight, backbone.blocks.7.norm1.bias, backbone.blocks.7.attn.qkv.weight, backbone.blocks.7.attn.qkv.bias, backbone.blocks.7.attn.proj.weight, backbone.blocks.7.attn.proj.bias, backbone.blocks.7.norm2.weight, backbone.blocks.7.norm2.bias, backbone.blocks.7.mlp.fc1.weight, backbone.blocks.7.mlp.fc1.bias, backbone.blocks.7.mlp.fc2.weight, backbone.blocks.7.mlp.fc2.bias, backbone.blocks.8.norm1.weight, backbone.blocks.8.norm1.bias, backbone.blocks.8.attn.qkv.weight, backbone.blocks.8.attn.qkv.bias, backbone.blocks.8.attn.proj.weight, backbone.blocks.8.attn.proj.bias, backbone.blocks.8.norm2.weight, backbone.blocks.8.norm2.bias, backbone.blocks.8.mlp.fc1.weight, backbone.blocks.8.mlp.fc1.bias, backbone.blocks.8.mlp.fc2.weight, backbone.blocks.8.mlp.fc2.bias, backbone.blocks.9.norm1.weight, backbone.blocks.9.norm1.bias, backbone.blocks.9.attn.qkv.weight, backbone.blocks.9.attn.qkv.bias, backbone.blocks.9.attn.proj.weight, backbone.blocks.9.attn.proj.bias, backbone.blocks.9.norm2.weight, backbone.blocks.9.norm2.bias, backbone.blocks.9.mlp.fc1.weight, backbone.blocks.9.mlp.fc1.bias, backbone.blocks.9.mlp.fc2.weight, backbone.blocks.9.mlp.fc2.bias, backbone.blocks.10.norm1.weight, backbone.blocks.10.norm1.bias, backbone.blocks.10.attn.qkv.weight, backbone.blocks.10.attn.qkv.bias, backbone.blocks.10.attn.proj.weight, backbone.blocks.10.attn.proj.bias, backbone.blocks.10.norm2.weight, backbone.blocks.10.norm2.bias, backbone.blocks.10.mlp.fc1.weight, backbone.blocks.10.mlp.fc1.bias, backbone.blocks.10.mlp.fc2.weight, backbone.blocks.10.mlp.fc2.bias, backbone.blocks.11.norm1.weight, backbone.blocks.11.norm1.bias, backbone.blocks.11.attn.qkv.weight, backbone.blocks.11.attn.qkv.bias, backbone.blocks.11.attn.proj.weight, backbone.blocks.11.attn.proj.bias, backbone.blocks.11.norm2.weight, backbone.blocks.11.norm2.bias, backbone.blocks.11.mlp.fc1.weight, backbone.blocks.11.mlp.fc1.bias, backbone.blocks.11.mlp.fc2.weight, backbone.blocks.11.mlp.fc2.bias, backbone.norm.weight, backbone.norm.bias, neck.fpn1.0.weight, neck.fpn1.0.bias, neck.fpn1.1.ln.weight, neck.fpn1.1.ln.bias, neck.fpn1.3.weight, neck.fpn1.3.bias, neck.fpn2.0.weight, neck.fpn2.0.bias, neck.fpn2.1.ln.weight, neck.fpn2.1.ln.bias, neck.fpn2.3.weight, neck.fpn2.3.bias, decode_head.conv_seg.weight, decode_head.conv_seg.bias, decode_head.convs.0.conv.weight, decode_head.convs.0.bn.weight, decode_head.convs.0.bn.bias, decode_head.convs.0.bn.running_mean, decode_head.convs.0.bn.running_var, decode_head.convs.0.bn.num_batches_tracked, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked, auxiliary_head.convs.1.conv.weight, auxiliary_head.convs.1.bn.weight, auxiliary_head.convs.1.bn.bias, auxiliary_head.convs.1.bn.running_mean, auxiliary_head.convs.1.bn.running_var, auxiliary_head.convs.1.bn.num_batches_tracked

missing keys in source state_dict: cls_token, pos_embed, patch_embed.proj.weight, patch_embed.proj.bias, blocks.0.norm1.weight, blocks.0.norm1.bias, blocks.0.attn.qkv.weight, blocks.0.attn.qkv.bias, blocks.0.attn.proj.weight, blocks.0.attn.proj.bias, blocks.0.norm2.weight, blocks.0.norm2.bias, blocks.0.mlp.fc1.weight, blocks.0.mlp.fc1.bias, blocks.0.mlp.fc2.weight, blocks.0.mlp.fc2.bias, blocks.1.norm1.weight, blocks.1.norm1.bias, blocks.1.attn.qkv.weight, blocks.1.attn.qkv.bias, blocks.1.attn.proj.weight, blocks.1.attn.proj.bias, blocks.1.norm2.weight, blocks.1.norm2.bias, blocks.1.mlp.fc1.weight, blocks.1.mlp.fc1.bias, blocks.1.mlp.fc2.weight, blocks.1.mlp.fc2.bias, blocks.2.norm1.weight, blocks.2.norm1.bias, blocks.2.attn.qkv.weight, blocks.2.attn.qkv.bias, blocks.2.attn.proj.weight, blocks.2.attn.proj.bias, blocks.2.norm2.weight, blocks.2.norm2.bias, blocks.2.mlp.fc1.weight, blocks.2.mlp.fc1.bias, blocks.2.mlp.fc2.weight, blocks.2.mlp.fc2.bias, blocks.3.norm1.weight, blocks.3.norm1.bias, blocks.3.attn.qkv.weight, blocks.3.attn.qkv.bias, blocks.3.attn.proj.weight, blocks.3.attn.proj.bias, blocks.3.norm2.weight, blocks.3.norm2.bias, blocks.3.mlp.fc1.weight, blocks.3.mlp.fc1.bias, blocks.3.mlp.fc2.weight, blocks.3.mlp.fc2.bias, blocks.4.norm1.weight, blocks.4.norm1.bias, blocks.4.attn.qkv.weight, blocks.4.attn.qkv.bias, blocks.4.attn.proj.weight, blocks.4.attn.proj.bias, blocks.4.norm2.weight, blocks.4.norm2.bias, blocks.4.mlp.fc1.weight, blocks.4.mlp.fc1.bias, blocks.4.mlp.fc2.weight, blocks.4.mlp.fc2.bias, blocks.5.norm1.weight, blocks.5.norm1.bias, blocks.5.attn.qkv.weight, blocks.5.attn.qkv.bias, blocks.5.attn.proj.weight, blocks.5.attn.proj.bias, blocks.5.norm2.weight, blocks.5.norm2.bias, blocks.5.mlp.fc1.weight, blocks.5.mlp.fc1.bias, blocks.5.mlp.fc2.weight, blocks.5.mlp.fc2.bias, blocks.6.norm1.weight, blocks.6.norm1.bias, blocks.6.attn.qkv.weight, blocks.6.attn.qkv.bias, blocks.6.attn.proj.weight, blocks.6.attn.proj.bias, blocks.6.norm2.weight, blocks.6.norm2.bias, blocks.6.mlp.fc1.weight, blocks.6.mlp.fc1.bias, blocks.6.mlp.fc2.weight, blocks.6.mlp.fc2.bias, blocks.7.norm1.weight, blocks.7.norm1.bias, blocks.7.attn.qkv.weight, blocks.7.attn.qkv.bias, blocks.7.attn.proj.weight, blocks.7.attn.proj.bias, blocks.7.norm2.weight, blocks.7.norm2.bias, blocks.7.mlp.fc1.weight, blocks.7.mlp.fc1.bias, blocks.7.mlp.fc2.weight, blocks.7.mlp.fc2.bias, blocks.8.norm1.weight, blocks.8.norm1.bias, blocks.8.attn.qkv.weight, blocks.8.attn.qkv.bias, blocks.8.attn.proj.weight, blocks.8.attn.proj.bias, blocks.8.norm2.weight, blocks.8.norm2.bias, blocks.8.mlp.fc1.weight, blocks.8.mlp.fc1.bias, blocks.8.mlp.fc2.weight, blocks.8.mlp.fc2.bias, blocks.9.norm1.weight, blocks.9.norm1.bias, blocks.9.attn.qkv.weight, blocks.9.attn.qkv.bias, blocks.9.attn.proj.weight, blocks.9.attn.proj.bias, blocks.9.norm2.weight, blocks.9.norm2.bias, blocks.9.mlp.fc1.weight, blocks.9.mlp.fc1.bias, blocks.9.mlp.fc2.weight, blocks.9.mlp.fc2.bias, blocks.10.norm1.weight, blocks.10.norm1.bias, blocks.10.attn.qkv.weight, blocks.10.attn.qkv.bias, blocks.10.attn.proj.weight, blocks.10.attn.proj.bias, blocks.10.norm2.weight, blocks.10.norm2.bias, blocks.10.mlp.fc1.weight, blocks.10.mlp.fc1.bias, blocks.10.mlp.fc2.weight, blocks.10.mlp.fc2.bias, blocks.11.norm1.weight, blocks.11.norm1.bias, blocks.11.attn.qkv.weight, blocks.11.attn.qkv.bias, blocks.11.attn.proj.weight, blocks.11.attn.proj.bias, blocks.11.norm2.weight, blocks.11.norm2.bias, blocks.11.mlp.fc1.weight, blocks.11.mlp.fc1.bias, blocks.11.mlp.fc2.weight, blocks.11.mlp.fc2.bias, norm.weight, norm.bias

Config file:

import os

custom_imports = dict(imports=["geospatial_fm"])

# base options
dist_params = dict(backend="nccl")
log_level = "INFO"
load_from = None
resume_from = None
cudnn_benchmark = True

dataset_type = "GeospatialDataset"

# TO BE DEFINED BY USER: data directory
data_root = "C:/Masterarbeit/hls-foundation-os/hls_burn_scars"

num_frames = 1
img_size = 224
num_workers = 4
samples_per_gpu = 4

img_norm_cfg = dict(
    means=[
        0.033349706741586264,
        0.05701185520536176,
        0.05889748132001316,
        0.2323245113436119,
        0.1972854853760658,
        0.11944914225186566,
    ],
    stds=[
        0.02269135568823774,
        0.026807560223070237,
        0.04004109844362779,
        0.07791732423672691,
        0.08708738838140137,
        0.07241979477437814,
    ],
)  # change the mean and std of all the bands

bands = [0, 1, 2, 3, 4, 5]
tile_size = 224
orig_nsize = 512
crop_size = (tile_size, tile_size)
img_suffix = "_merged.tif"
seg_map_suffix = ".mask.tif"
ignore_index = -1
image_nodata = -9999
image_nodata_replace = 0
image_to_float32 = True

# model
# TO BE DEFINED BY USER: model path
pretrained_weights_path = "C:/Masterarbeit/burn_scars_Prithvi_100M.pth"
num_layers = 12
patch_size = 16
embed_dim = 768
num_heads = 12
tubelet_size = 1
output_embed_dim = num_frames * embed_dim
max_intervals = 10000
evaluation_interval = 1000

# TO BE DEFINED BY USER: model path
experiment = "TEST"
project_dir = "C:\Masterarbeit"
work_dir = os.path.join(project_dir, experiment)
save_path = work_dir

save_path = work_dir
train_pipeline = [
    dict(type="LoadGeospatialImageFromFile", to_float32=image_to_float32),
    dict(type="LoadGeospatialAnnotations", reduce_zero_label=False),
    dict(type="BandsExtract", bands=bands),
    dict(type="RandomFlip", prob=0.5),
    dict(type="ToTensor", keys=["img", "gt_semantic_seg"]),
    # to channels first
    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
    dict(type="TorchNormalize", **img_norm_cfg),
    dict(type="TorchRandomCrop", crop_size=(tile_size, tile_size)),
    dict(
        type="Reshape",
        keys=["img"],
        new_shape=(len(bands), num_frames, tile_size, tile_size),
    ),
    dict(type="Reshape", keys=["gt_semantic_seg"], new_shape=(1, tile_size, tile_size)),
    dict(type="CastTensor", keys=["gt_semantic_seg"], new_type="torch.LongTensor"),
    dict(type="Collect", keys=["img", "gt_semantic_seg"]),
]
test_pipeline = [
    dict(type="LoadGeospatialImageFromFile", to_float32=image_to_float32),
    dict(type="BandsExtract", bands=bands),
    dict(type="ToTensor", keys=["img"]),
    # to channels first
    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
    dict(type="TorchNormalize", **img_norm_cfg),
    dict(
        type="Reshape",
        keys=["img"],
        new_shape=(len(bands), num_frames, -1, -1),
        look_up=dict({"2": 1, "3": 2}),
    ),
    dict(type="CastTensor", keys=["img"], new_type="torch.FloatTensor"),
    dict(
        type="CollectTestList",
        keys=["img"],
        meta_keys=[
            "img_info",
            "seg_fields",
            "img_prefix",
            "seg_prefix",
            "filename",
            "ori_filename",
            "img",
            "img_shape",
            "ori_shape",
            "pad_shape",
            "scale_factor",
            "img_norm_cfg",
        ],
    ),
]

CLASSES = ("Unburnt land", "Burn scar")

data = dict(
    samples_per_gpu=samples_per_gpu,
    workers_per_gpu=num_workers,
    train=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir="training",
        ann_dir="training",
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=train_pipeline,
        ignore_index=-1,
    ),
    val=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir="validation",
        ann_dir="validation",
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=test_pipeline,
        ignore_index=-1,
    ),
    test=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir="validation",
        ann_dir="validation",
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=test_pipeline,
        ignore_index=-1,
    ),
)

optimizer = dict(type="Adam", lr=1.3e-05, betas=(0.9, 0.999))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy="poly",
    warmup="linear",
    warmup_iters=1500,
    warmup_ratio=1e-06,
    power=1.0,
    min_lr=0.0,
    by_epoch=False,
)
log_config = dict(
    interval=20,
    hooks=[
        dict(type="TextLoggerHook", by_epoch=False),
        dict(type="TensorboardLoggerHook", by_epoch=False),
    ],
)
checkpoint_config = dict(by_epoch=True, interval=10, out_dir=save_path)
evaluation = dict(
    interval=evaluation_interval,
    metric="mIoU",
    pre_eval=True,
    save_best="mIoU",
    by_epoch=False,
)

loss_func = dict(type="DiceLoss", use_sigmoid=False, loss_weight=1, ignore_index=-1)

runner = dict(type="IterBasedRunner", max_iters=max_intervals)
workflow = [("train", 1)]
norm_cfg = dict(type="BN", requires_grad=True)
model = dict(
    type="TemporalEncoderDecoder",
    frozen_backbone=False,
    backbone=dict(
        type="TemporalViTEncoder",
        pretrained=pretrained_weights_path,
        img_size=img_size,
        patch_size=patch_size,
        num_frames=num_frames,
        tubelet_size=tubelet_size,
        in_chans=len(bands),
        embed_dim=embed_dim,
        depth=12,
        num_heads=num_heads,
        mlp_ratio=4.0,
        norm_pix_loss=False,
    ),
    neck=dict(
        type="ConvTransformerTokensToEmbeddingNeck",
        embed_dim=embed_dim * num_frames,
        output_embed_dim=output_embed_dim,
        drop_cls_token=True,
        Hp=14,
        Wp=14,
    ),
    decode_head=dict(
        num_classes=len(CLASSES),
        in_channels=output_embed_dim,
        type="FCNHead",
        in_index=-1,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        norm_cfg=dict(type="BN", requires_grad=True),
        align_corners=False,
        loss_decode=loss_func,
    ),
    auxiliary_head=dict(
        num_classes=len(CLASSES),
        in_channels=output_embed_dim,
        type="FCNHead",
        in_index=-1,
        channels=256,
        num_convs=2,
        concat_input=False,
        dropout_ratio=0.1,
        norm_cfg=dict(type="BN", requires_grad=True),
        align_corners=False,
        loss_decode=loss_func,
    ),
    train_cfg=dict(),
    test_cfg=dict(
        mode="slide",
        stride=(int(tile_size / 2), int(tile_size / 2)),
        crop_size=(tile_size, tile_size),
    ),
)
auto_resume = False

I followed the instruction on README. I got this error (tried with Prithvi_100M.pt, also the same thing). What did I do wrong? Thanks

[franrubi]

Hi, are you using 'burn_scars_Prithvi_100M.pth' file (1,2GB size), available from https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar/tree/main?

[longdaothanh]

Hi, are you using 'burn_scars_Prithvi_100M.pth' file (1,2GB size), available from https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar/tree/main?

Yes, can still train but just found it interesing

[longdaothanh]

Hi, are you using 'burn_scars_Prithvi_100M.pth' file (1,2GB size), available from https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar/tree/main?

Hi, I have one question: Do you have any ideas how to get feature importance and permutation feature importance?